Systems and methods for analyzing pathologies utilizing quantitative imaging

ABSTRACT

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The subject application relates and claims priority to U.S. ProvisionalApplication Ser. Nos. 62/205,322, 62/205,313, 62/205,305, 62/205,295 and62/219,860, the contents of which are incorporated herein in theirentirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH/DEVELOPMENT

This work supported in part by NSF SBIR Award 1248316 and NIH SBIR AwardR44 HL126224-01A1 and the government may have certain rights to thework.

BACKGROUND OF THE INVENTION

The present disclosure related to quantitative imaging and analytics.More specifically, the present disclosure relates to systems and methodsfor analyzing pathologies utilizing quantitative imaging.

Imaging, particularly with safe and non-invasive methods, represents themost powerful methods for locating the disease origin, capturing itsdetailed pathology, directing therapy, and monitoring progression tohealth. Imaging is also an extremely valuable and low cost method tomitigate these human and financial costs by allowing for appropriateearly interventions that are both less expensive and disruptive.

Enhanced imaging techniques have made medical imaging an essentialcomponent of patient care. Imaging is especially valuable because itprovides spatially- and temporally-localized anatomic and functionalinformation, using non- or minimally invasive methods. However,techniques to effectively utilize increasing spatial and temporalresolution are needed, both to exploit patterns or signatures in thedata not readily assessed with the human eye as well as to manage thelarge magnitude of data in such a way as to efficiently integrate itinto the clinical workflow. Without aid, the clinician has neither thetime nor often the ability to effectively extract the informationcontent which is available, and in any case generally interprets theinformation subjectively and qualitatively. Integrating quantitativeimaging for individual patient management as well as clinical trials fortherapy development requires a new class of decision support informaticstools to enable the medical community to fully exploit the capabilitiesof made possible with the evolving and growing imaging modalities withinthe realities of existing work flows and reimbursement constraints.

Quantitative results from imaging methods have the potential to be usedas biomarkers in both routine clinical care and in clinical trials, forexample, in accordance with the widely accepted NIH Consensus Conferencedefinition of a biomarker. In clinical practice, quantitative imagingare intended to (a) detect and characterize disease, before, during orafter a course of therapy, and (b) predict the course of disease, withor without therapy. In clinical research, imaging biomarkers may be usedin defining endpoints of clinical trials.

Quantification builds on imaging physics developments which haveresulted in improvements of spatial, temporal, and contrast resolutionas well as the ability to excite tissues with multipleenergies/sequences, yielding diverse tissue-specific responses. Theseimprovements thereby allow tissue discrimination and functionalassessment, and are notably seen, for example, in spectral computedtomography (spectral CT), multi-contrast magnetic resonance imaging(multi-contrast MRI), ultrasound (US), and targeted contrast agentapproaches with various imaging modalities. Quantitative imagingmeasures specific biological characteristics that indicate theeffectiveness of one treatment over another, how effective a currenttreatment is, or what risk a patient is at should they remain untreated.Viewed as a measurement device, a scanner combined with image processingof the formed images has the ability to measure characteristics oftissue based on the physical principles relevant to a given imagingapproach and how differing tissues respond to them. Though the imageformation process differs widely across modalities, some generalizationshelp frame the overall assessment, though exceptions, nuances, andsubtleties drive the real conclusions and until and unless they areconsidered some of the greatest opportunities are missed.

Imaging in the early phases of clinical testing of novel therapeuticscontributes to the understanding of underlying biological pathways andpharmacological effects. It may also reduce the cost and time needed todevelop novel pharmaceuticals and therapeutics. In later phases ofdevelopment, imaging biomarkers may serve as important endpoints forclinical benefit. In all phases, imaging biomarkers may be used toselect or stratify patients based on disease status, in order to betterdemonstrate therapeutic effect.

SUMMARY

Systems and methods are provided herein which utilize a hierarchicalanalytics framework to identify and quantify biologicalproperties/analytes from imaging data and then identify and characterizeone or more medical conditions based on the quantified biologicalproperties/analytes. In some embodiments, the systems and methodsincorporate computerized image analysis and data fusion algorithms withpatient clinical chemistry and blood biomarker data to provide amulti-factorial panel that may be used to distinguish between differentsubtypes of disease. Thus, the systems and methods of the presentdisclosure may advantageously implement biological and clinical insightsin advanced computational models. These models may then interface withsophisticated image processing through rich ontologies that specifytechnical factors associated with the growing understanding ofpathogenesis and takes the form of rigorous definitions of what is beingmeasured and how it is measured and assessed and how it is relates toclinically-relevant subtypes and stages of disease.

Human disease exhibits strong phenotypic differences that can beappreciated by applying sophisticated classifiers on extracted featuresthat capture spatial, temporal, and spectral results measurable byimaging but difficult to appreciate unaided. Traditional Computer-AidedDiagnostics make inferences in a single step from image features. Incontrast, the systems and methods of the present disclosure employ ahierarchical inference scheme including intermediary steps ofdetermining spatial image features and time-resolved kinetics atmultiple levels of biologically-objective components of morphology,composition and structure which in subsequently are utilized to drawclinical inferences. Advantageously, the hierarchical inference schemeensures the clinical inferences can be understood, validated, andexplained at each level in the hierarchy.

In example embodiments, system and methods are provided which utilize aprocessor a non-transient storage medium including processor executableinstructions implementing an analyzer module including a hierarchicalanalytics framework configured to (i) utilize a first set of algorithmsidentify and quantify a set of biological properties utilizing imagingdata and (ii) utilize a second set of algorithms to identify andcharacterize one or more medical conditions based on the quantifiedbiological properties. In some embodiments the analytics framework mayimplement an algorithm for identifying and characterizing the one ormore medical conditions based on the quantified biological propertieswherein a training set from one or more non-radiological or non-imagingdata sources was used in training the algorithm. In other the analyticsframework may implement an algorithm for identifying and quantifying thebiological properties utilizing radiological imaging data, wherein atraining set from one or more non-radiological data sources was usedtraining the algorithm.

In example embodiments, data from a plurality of same or different typesof data sources may be incorporated into the process of identifying andcharacterizing the one or more medical conditions. In some embodiments,data from one or more non-imaging data sources may be used inconjunction with the imaging data such that the set of biologicalproperties includes one or more biological properties identified orquantified based at least in part on the data from one or morenon-imaging data sources. For example, data from non-imaging sources mayinclude one or more of (i) demographics, (ii) results from cultures orother lab tests, (iii) genomic, proteomic or metabolomic expressionprofiles, or (iv) diagnostic observations. In some embodiments, datafrom one or more non-radiological data sources may be used inconjunction with radiological imaging data such that the set ofbiological properties includes one or more biological propertiesidentified or quantified based at least in part on the data from one ormore non-radiological data sources.

In example embodiments, information relating to the set of identifiedand quantified biological properties may be adjusted after an initialidentification or quantification thereof based on contextual informationwhich adjusts or updates one or more probabilities impacting theidentification or quantification of at least one of the biologicalproperties in the set. For example, the contextual information includesat least one of patient demographics, correlations relating differentbiological properties, or correlations relating one or more of theidentified medical conditions to one or more biological properties. Insome embodiments, information relating to the identified andcharacterized one or more medical conditions may be adjusted after aninitial identification or characterization thereof based on contextualinformation which adjusts or updates one or more probabilities impactingthe identification or characterization of at least one of one or moremedical conditions.

In example embodiments, the systems and methods of the presentdisclosure may be configured to provide a user with information relatingboth the one or more medical conditions as well as relating to theunderlying biological properties used in the identification orcharacterization of the one or more medical conditions.

In example embodiments, the systems and methods of the presentdisclosure may be configured to determine at least one of (i) which ofthe biological parameters in the set have the greatest amount ofuncertainty regarding the identification or quantification thereof or(ii) which of the biological parameters in the set are mostdeterministic of the identification or characterization of the one ormore medical conditions. Thus, the systems and methods of the presentdisclosure may advantageously provide advice, e.g., relating to furtherdiagnostics based on such determinations.

In example embodiments, the identifying and quantifying the set ofbiological properties utilizing the imaging data may include receivingpatient data including the image data and parsing the received data intoa set of empirical parameters including one or more imaging features ofan imaged target. For example, the parsing the received data may includepre-processing image data including performing one or more of: (i)intensity vector analysis, (ii) image registration and transformationanalysis or (iii) anatomic region analysis and imaging features may bederived derived based on one or more of: (i) temporal operators, (ii)fractal analysis, (iii) spatial operators or (iv) or an augmented Markovanalysis.

In example embodiments, the set of biological properties may incldue oneor more anatomical, morphological, structural, compositional,functional, chemical, biochemical, physiological, histological orgenetic characteristics. In some embodiments, an imaged target may be alesion and wherein the biological properties include (i) a size of thelesion, (ii) a shape of the lesion, (iii) a characterization of themargin of the lesion, (iv) a solidity of the lesion, (v) a heterogeneityof the lesion, (vi) a measure of the lesion's invasive extent orpotential extent, (vii) a compositional measure of calcification relatedto the lesion and (viii) a measure of cell metabolism with respect tothe lesion. In other embodiments, an imaged target may be a blood vesseland wherein the biological properties include (i) an indication ofplaque coverage of the vessel wall, (ii) an indication of stenosis ofthe vessel wall, (iii) an indication of dilation of the vessel wall, and(iv) an indication of vessel wall thickness. In yet further embodiments,an imaged target may be a vascular tissue and wherein the biologicalproperties include (i) an indication of a lipid core of the vascular orrelated tissue, (ii) a measure of fibrosis of the vascular or relatedtissue, (iii) a measure of calcification of the vascular or relatedtissue, (iv) an indication of any hemorrhage in the vascular or relatedtissue, (v) a measure of permeability of the vascular or related tissue,(vi) an indication of thrombosis of the vascular or related tissue, and(vii) an indication of ulceration of the vascular or related tissue. Insome embodiments, at least one or the biological properties may bequantified by (i) assessing change between a plurality of timepoints or(ii) assessing differences between a plurality of targets.

In example embodiments, the characterization of the one or more medicalconditions may include phenotyping the medical conditions. In someembodiments, the characterization of the one or more medical conditionsmay further include determining predictive outcomes for the medicalconditions. For example, the one or more predictive outcomes may bepredicated on a predetermined causality rating between phenotypes andthe predictive outcomes.

In example embodiments, the storage medium may further include processorexecutable instructions implementing a trainer module, for training oneor more algorithms implemented by the hierarchical analytics framework.In further example embodiments the storage medium may further includeprocessor executable instructions implementing a cohort module forenabling a user to define one or more cohort groupings of individualsfor further analysis.

In example embodiments, the analyzer module may include algorithms forcalculating imaging features from the imaging data, wherein some of theimaging features are computed on a per-pixel basis, while other imagingfeatures are computed on a region-of-interest basis. In someembodiments, the first set of algorithms is distinctly trained from thesecond set of algorithms. In example embodiments, at least one of thealgorithms in the first and second sets of algorithms may be derivedutilizing machine learning. For example, at least one of the algorithmsin the first and second sets of algorithms may be characterized by oneor more of neural nets, SVMs, partial least squares, principlecomponents analysis or random forests.

In example embodiments, the analyzer module may be configured to enabledelineating of a field for the imaging data. In some embodiment, thedelineating the field may include segmenting one of organs, vessels,lesion or other application-specific anatomical features. For example,the field may be a cross-sectional slice of a blood vessel. In someembodiments, the analyzer module may be further configured to delineatea target in the field and determining anatomic structure or compositioncharacteristics for the target, wherein the target is a blob in thecross-sectional slice of a blood vessel.

In example embodiments, the hierarchical analytics framework nay includefitting a biological model utilizing the imaging data wherein thebiological model is then utilized to identify and quantify thebiological properties. In some embodiments, the model may be a fractalmodel. In other embodiments, the model may be based on hybridBayesian/Markovian network. In example embodiments, the model maycompute biological parameters one or more contiguous regions of a givenanalyte type. In some embodiments, the model may further computebiological parameters based on relationships between two- or moredifferent contiguous regions of a given analyte type or given analytetypes. In further embodiments, the model may also compute biologicalparameters based on a number of contiguous regions of a given analytetype or given analyte types. In some embodiments, the model may employan expectation maximization which accounts for conditional dependencebetween pixels.

In example embodiments, a non-transient storage medium is disclosedincluding processor executable instructions for (i) receiving patientdata including a set of empirical parameters, the set of empiricalparameters including one or more imaging features of an imaged target;(i) utilizing a first algorithm to identify and quantify one or morelogical characteristics indicated by the empirical parameters, thelogical characteristics representing pathological features; (ii)identifying a set of pathological features, the set of pathologicalfeatures including the one or more quantified logical characteristics;and (iii) utilizing a second algorithm to identify one or morepathologies indicated by the set of pathological features.

In example embodiments, the first algorithm may be derived utilizing atraining collection of a plurality of sets of empirical parameters eachwith associated with known quantifications of one or more pathologicalfeatures. In some embodiments, the first algorithm may include a scoringalgorithm for determining a confidence weighting for each of the logicalcharacteristics. For example, the confidence weighting for each logicalcharacteristic may incldue a confidence weighting for a quantificationof that logical characteristic. In some embodiments, the confidenceweighting for the quantification of the logical characteristic may bedetermined according to a probability distribution across a range ofvalues for the logical characteristic. In example embodiments, aconfidence threshold may be utilized to identify the logicalcharacteristics indicated by the empirical parameters.

In example embodiments, the second algorithm may include a scoringalgorithm for determining a confidence weighting for each of thepathologies. For example, the confidence weighting for each pathologymay include a confidence weighting for a phenotype thereof. In someembodiments, the confidence weighting for the phenotype may bedetermined according to a probability distribution across a range ofphenotypes for the pathology. In example embodiments, a confidencethreshold may be utilized to identify the pathologies indicated by thepathological features.

In example embodiments, an initial confidence weighting in a firstpathology may be used to adjust an initial confidence weighting in asecond related pathology. For example, an initial confidence weightingin the first pathology may be used to adjust an initial confidenceweighting in a logical characteristic and wherein the adjustedconfidence weighting in the logical characteristic may then be used toindicate the second related pathology.

In example embodiments, the first and second algorithms may be trainedutilizing one or more of empirical data or expert opinion. In someembodiments, the first and second algorithms may be characterized by oneor more of machine learning, decision trees, differential equations,polynomial expressions, pattern matching or parsing, dynamicprogramming, or state space searches.

In example embodiments, a system is disclosed the system including animaging device for imaging a target; a processor configured for: (i)receiving patient data including a set of empirical parameters, the setof empirical parameters including one or more imaging features of theimaged target; (ii) utilizing a first machine learned algorithm toidentify and quantify one or more logical characteristics indicated bythe empirical parameters, the logical characteristics representingpathological features; (iii) identifying a set of pathological features,the set of pathological features including the one or more quantifiedlogical characteristics; and (iv) utilizing a second machine learnedalgorithm to identify one or more pathologies indicated by the set ofpathological features; and a user interface for outputting informationrelating to the one or more identified pathologies.

In example embodiments, a processor enabled method is disclosed, themethod including identifying a set of empirical parameters, the set ofempirical parameters including one or more imaging features of theimaged target; utilizing a first machine learned algorithm to identifyand quantify one or more logical characteristics indicated by theempirical parameters, the logical characteristics representingpathological features; identifying a set of pathological features, theset of pathological features including the one or more quantifiedlogical characteristics; and utilizing a second machine learnedalgorithm to identify one or more pathologies indicated by the set ofpathological features.

While the systems and methods of the present disclosure have beenparticularly shown and described with reference to example embodimentsthereof, it will be understood by those skilled in the art that variouschanges in form and details may be made therein without departing fromthe scope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will be apparent from the following more particulardescription of example embodiments, as illustrated in the accompanyingdrawings in which like reference characters refer to the same partsthroughout the different views. The drawings are not necessarily toscale, emphasis instead being placed upon illustrating embodiments ofthe present disclosure.

FIG. 1 depicts a schematic of an exemplary system for determining andcharacterizing a medical condition by implementing a hierarchicalanalytics framework, according to the present disclosure.

FIG. 2 outlines a re-sampling based model building approach, accordingto the present disclosure which may be implemented by the systems andmethods described herein.

FIG. 3 depicts a sample patient report, according to the presentdisclosure which may be outputted by the systems and methods describedherein.

FIG. 4 depicts example segmentation levels for a multi-scale vessel wallanalyte map, according to the present disclosure.

FIG. 5 depicts an exemplary pixel-level probability mass function as aset of analyte probability vectors, according to the present disclosure.

FIG. 6 illustrates a technique for computing putative analyte blobs,according to the present disclosure.

FIG. 7 depicts normalized vessel wall coordinates for an exemplaryvessel wall composition model, according to the present disclosure.

FIG. 8 depicts an example margin between plaque removed for a histologyspecimen and the outer vessel wall, according to the present disclosure.

FIG. 9 illustrates some complex vessel topologies which can be accountedfor using the techniques described herein, according to the presentdisclosure.

FIG. 10 depicts representing an exemplary analyte blob with adistribution of normalized vessel wall coordinates, according to thepresent disclosure.

FIG. 11 depicts an exemplary distribution of blog descriptors, accordingto the present disclosure.

FIG. 12 depicts an exemplary model for imaging data correlating betweena hidden ground truth state and an observed state, according to thepresent disclosure.

FIG. 13 depicts a diagram of an example Markov modelNiterbi algorithmfor relating an observed state to a hidden state in an image model,according to the present disclosure.

FIG. 14 depicts an example frequency distribution of total number ofblobs per histological slide for a plurality of histological slides,according to the present disclosure.

FIG. 15 depicts exemplary implantation of a 1D Markov chain, accordingto the present disclosure.

FIG. 16 depicts an example first order Markov chain for a textprobability table, according to the present disclosure.

FIG. 17 depicts conditional dependence of a first pixel based on itsneighboring pixels, according to the present disclosure.

FIG. 18 depicts a further exemplary hierarchical analytics frameworkaccording to the present disclosure.

DETAILED DESCRIPTION

Systems and methods for analyzing pathologies utilizing quantitativeimaging are presented herein. Advantageously, the systems and methods ofthe present disclosure utilize a hierarchical analytics framework thatidentifies and quantify biological properties/analytes from imaging dataand then identifies and characterizes one or more pathologies based onthe quantified biological properties/analytes. This hierarchicalapproach of using imaging to examine underlying biology as anintermediary to assessing pathology provides many analytic andprocessing advantages over systems and methods that are configured todirectly determine and characterize pathology from underlying imagingdata.

One advantage, for example, is the ability to utilize training sets fromnon-radiological sources, e.g., from tissue sample sources such ashistological information, in conjunction with or independent of trainingsets for radiological sources, to correlate radiological imagingfeatures to biological properties/analytes to pathologies. For example,in some embodiments, histology information may be used in trainingalgorithms for identifying and characterizing one or more pathologiesbased on quantified biological properties/analytes. More specifically,biological properties/analytes which are identifiable/quantifiable innon-radiological data (such as in an invasively obtained histology dataset or obtainable via gene expression profiling) may also be identifiedand quantified in radiological data (which is advantageouslynon-invasive). These biological properties/analytes may then becorrelated to clinical findings on pathology using information the fromnon-radiological sources, for example, utilizing histologicalinformation, gene expression profiling, or other clinically rich datasets. This set of clinically correlated data may then serve as atraining set or part of a training set for determining/tuning (e.g.,utilizing machine learning) algorithms correlating biologicalproperties/analytes to pathologies with a known relationship to clinicaloutcome. These algorithms correlating biological properties/analytes topathologies derived utilizing non-radiological source training sets maythen be applied in evaluating biological properties/analytes derivedfrom radiological data. Thus, the systems and methods of the presentdisclosure may advantageously enable utilizing radiological imaging(which may advantageously be cost-effective and non-invasive) to providesurrogate measures for predicting clinical outcome.

Notably, in some instances training data for non-radiological sources(such as histology information) may be more accurate/reliable thantraining data for radiological sources. Moreover, in some embodiments,training data from non-radiological sources may be used to augmenttraining data from radiological sources. Thus, since better data in islikely to yield better data out, the hierarchical analytics frameworkdisclosed advantageously improves the trainability and resultingreliability of the algorithms disclosed herein. As noted above, one keyadvantage is that, once trained the systems and methods of the presentdisclosure may enable deriving comparable clinical information toexisting histological and other non-radiological diagnostic-type testingwithout the need not undergo invasive and/or costly procedures.

Alternatively, in some embodiments, training sets for non-radiologicalsources (such as non-radiological imaging sources, e.g., histologicalsources, and/or non-imaging sources) may be utilized in conjunction withor independent of training sets for radiological sources, e.g., incorrelating image features to biological properties/analytes. Forexample in some embodiments one or more biological models may beextrapolated and fitted to correlate radiological and non-radiologicaldata. For example, histology information may be correlated withradiological information based on an underlying biological model. This,correlation may advantageously enable training recognition of biologicalproperties/analytes in radiological data utilizing non-radiological,e.g., histological information.

In some embodiments, data drawn from complementary modalities may beused, e.g., in correlating image features to biologicalproperties/analytes from blood panels and/or other sources of data.

In example embodiments one or more biological models may be extrapolatedand fitted utilizing imaging data drawn from one imaging modality eithercorrelated with and/or fused with another imaging modality ornon-imaging source such as bloodwork. These biological models mayadvantageously correlate across and between imaging and non-imaging datasets based on the biological models. Thus, these biological models mayenable the hierarchical analytics framework to utilize data from oneimaging modality with another imaging modality or with a non-imagingsource in identifying/quantifying one or more biologicalproperties/analytes or identifying/characterizing one or more medicalconditions.

Another advantage to the hierarchical analytics framework disclosedherein, is the ability to incorporate data from multiple same ordifferent type data sources into the process of identifying andcharacterizing pathology based on imaging data. For example, in someembodiments, one or more non-imaging data sources may be used inconjunction with one or more imaging data sources in identifying andquantifying a set of biological properties/analytes. Thus, inparticular, the set of biological properties/analytes may include one ormore biological properties/analytes identified and/or quantified basedon one or more imaging data sources, one or more biologicalproperties/analytes identified and/or quantified based on one or morenon-imaging data sources, and/or one or more biologicalproperties/analytes identified and/or quantified based on a combinationof imaging and non-imaging data sources (note that, for the purposes ofthe quantitative imaging systems and methods of the present disclosurethe set of biological properties/analytes may generally include at leastone or more biological properties/analytes identified and/or quantifiedbased at least in part on an imaging data). The ability to augmentinformation from an imaging data source with information from otherimaging and/or non-imaging data sources in identifying and quantifying aset of biological properties/analytes adds to the robustness of thesystems and methods presented herein and enables utilization of any andall relevant information in identifying and characterizing pathology.

Yet another advantage of the hierarchical analytics framework involvesthe ability to adjust/fine-tune data at each level, e.g., prior orsubsequent to utilizing that data to assess the subsequent level (notethat in some embodiments this may be an iterative process). For example,in some embodiments, information related to a set of identified andquantified biological properties/analytes may be adjusted in an aposteriori manner (e.g., after an initial identification and/orquantification thereof). Similarly, in some embodiments, informationrelated to a set of identified and characterized pathologies may beadjusted in an a posteriori manner (e.g., after an initialidentification and/or characterization thereof). These adjustments maybe automatic or user based and may objective or subjective. The abilityto adjust/fine-tune data at each level may advantageously improve dataaccountability and reliability.

In example embodiments, adjustments may be based on contextualinformation, which may be used to update one or more probabilitiesimpacting a determination or quantification of a biologicalproperty/analyte. In example embodiments, contextual information foradjusting information related to a set of identified and quantifiedbiological properties/analytes in an a posteriori manner may includepatient demographics, correlations between biologicalproperties/analytes or correlations between identified/characterizedpathologies and biological properties/analytes. For example, in someinstances the biological properties/analytes may be related in the sensethat the identification/quantification of a first biologicalproperty/analyte may impact a probability relating theidentification/quantification of a second biological property/analyte.In other instances, identification/characterization of a firstpathology, e.g., based on an initial set of identified/quantifiedbiological properties/analytes may impact a probability relating to theidentification/quantification of a biological property/analyte in theinitial set or even a biological property/analyte that wasn't in thefirst set. In further instances, pathologies may be related, e.g.,wherein identification/characterization of a first pathology may impacta probability relating the identification/characterization of a firstpathology. As noted above, information related to identification andquantification of biological properties/analytes and/or informationrelated to the identification and characterization of pathologies may beupdated in an iterative manner, e.g., until data convergence orthresholds/benchmarks are achieved or for a selected number of cycles.

A further advantage of the hierarchical analytics framework involves theability to provide a user, e.g., a physician, with information relatingboth to a pathology as well as the underlying biology. This addedcontext may facilitate clinical diagnosis/evaluation as well asassessing/determining next steps, e.g., therapeutic/treatment options orfurther diagnostics. For example, the systems and methods may beconfigured to determine which biological parameters/analytes relevant tothe identification/quantification of one or more pathologies are mostindeterminate/have the highest degree of uncertainty (e.g., by reason oflack of data or conflicting data). In such instances, specific furtherdiagnostics may be recommended. The added context of providing a userwith information relating both to a pathology as well as the underlyingbiology may further help the user evaluate/error check various theclinical conclusions and recommendations reached by the analytics.

A hierarchical analytics framework, as used herein, refers to ananalytic framework wherein a one or more intermediary sets of datapoints are utilized as an intermediary processing layer or anintermediary transformation between initial set of data points and anend set of data points. This is similar to the concept of deep learningor hierarchical learning wherein algorithms are used to model higherlevel abstractions using multiple processing layers or otherwiseutilizing multiple transformations such as multiple non-lineartransformations. In general, the hierarchical analytics framework of thesystems and methods of the present disclosure includes data pointsrelating to biological properties/analytes as an intermediary processinglayer or intermediary transformation between imaging data points andpathology data points, in example, embodiments, multiple processinglayers or multiple transformation (e.g., as embodied by multiple levelsof data points) may be included for determining each of imaginginformation, underlying biological information and pathologyinformation. While example hierarchical analytic framework structuresare introduced herein (e.g., with specific processing layers, transformsand datapoints), the systems and methods of the present disclosure arenot limited to such implementations. Rather, any number of differenttypes of analytic framework structures may be utilized without departingfrom the scope and spirit of the present disclosure. In exampleembodiments, the hierarchical analytics frameworks of the subjectapplication may be conceptualized as including a logical data layer asan intermediary between an empirical data layer (including imaging data)and a results layer (including pathology information). Whereas theempirical data layer represents directly sourced data the logical datalayer advantageously adds a degree of logic and reasoning which distillsthis raw data into a set of useful analytes for the results layer inquestion. Thus, for example, empirical information from diagnostics suchas raw imaging information may be advantageously distilled down to alogical information relating to a particular set of biological featureswhich is relevant for assessing a selected pathology or group ofpathologies (for example, pathologies related to an imaged region of thepatient's body). In this way the biological features/analytes of thesubject application can also be thought of as pathologysymptoms/indicators.

The biological features/analytes of the subject application may at timesbe referred to herein a biomarkers. While the term “biological” orprefix “bio” is used in characterizing biological features or biomarkersthis in only intended to signify that the features or markers have adegree of relevance with respect to the patient's body. For example,biological features may be anatomical, morphological, compositional,functional, chemical, biochemical, physiological, histological, geneticor any number of other types of features related to the patient's body.Example, biological features utilized by specific implementations of thesystems and methods of the present disclosure (e.g., as relating toparticular anatomical regions of a patient such as the vascular system,the respiratory system, organs such as the lungs, heart or kidneys, orother anatomical regions) are disclosed herein.

While example systems and methods of the present disclosure may begeared toward detecting, characterizing and treatingpathologies/diseases, the application of the systems and methods of thepresent disclosure are not limited to pathologies/diseases but rathermay more generally applicable with respect to any clinically relevantmedical conditions of a patient including, e.g., syndromes, disorders,traumas, allergic reactions, etc.

In exemplary embodiments, the systems and methods of the presentdisclosure relate to Computer-Aided Phenotyping, e.g., by usingknowledge about biology to analyze medical images to measure thedifferences between disease types that have been determined throughresearch to indicate phenotypes which in turn predict outcomes. Thus, insome embodiments, characterizing pathologies may include determiningphenotypes for the pathologies which may in turn determine a predictiveoutcome.

With initial reference to FIG. 1, a schematic of an exemplary system 100is depicted. There are three basic functionalities which may be providedby the system 100 as represented by the trainer module 110, the analyzermodule 120 and the cohort tool module 130. As depicted, the analyzermodule 120 advantageously implements a hierarchical analytics frameworkwhich first identifies and quantifies biological properties/analytes 130utilizing a combination of (i) imaging features 122 from one or moreacquired images 121A of a patient 50 and (ii) non-imaging input data121B for a patient 50 and then identifies and characterizes one or morepathologies (e.g., prognostic phenotypes) 124 based on the quantifiedbiological properties/analytes 123. Advantageously, the analyzer module120 may operate independent of ground truth or validation references byimplementing one or more pre-trained, e.g., machine learned algorithmsfor drawing its inferences.

In example embodiments, the analyzer may include algorithms forcalculating imaging features 122 from the acquired images 121A of thepatient 50. Advantageously, some of the image features 122 may becomputed on a per-voxel basis while others may be computed on aregion-of-interest basis. Example non-imaging inputs 121B which may beutilized along with acquired images 121A may include data fromlaboratory systems, patient-reported symptoms, or patient history.

As noted above, the image features 122 and non-imaging inputs may beutilized by the analyzer module 120 to calculate the biologicalproperties/analytes 123. Notably, the biological properties/analytes aretypically quantitative, objective properties (e.g., objectivelyverifiable rather than being stated as impression or appearances) thatmay represent e.g., a presence and degree of a marker (such as achemical substance) or other measurements such as structure, size, oranatomic characteristics of region of interest. In example embodiments,the quantified biological properties/analytes 123 may be displayed orexported for direct consumption by the user, e.g., by a clinician, inaddition to or independent of further processing by the analyzer module.

In example embodiments, one or more of the quantified biologicalproperties/analytes 123 may be used as inputs for determining phenotype.Phenotypes are typically defined in a disease-specific mannerindependent of imaging, often being drawn from ex vivopathophysiological samples for which there is documented relationship tooutcome expected. In example embodiments, the analyzer module 120 mayalso provide predicted outcomes 125 for determined phenotypes.

It should be appreciated that example implementations of the analyzermodule 120 are further described herein with respect to specificembodiments which follow the general description of the system 100. Inparticular, specific imaging features, biological properties/analytesand pathologies/phenotypes are described with respect to specificmedical applications such as with respect to the vascular system or withrespect to the respiratory system.

With reference still to FIG. 1, the cohort tool module 130 enablesdefining a cohort of patients for group analyses thereof, e.g., based ona selected set of criteria related to the cohort study in question. Anexample cohort analysis may be for a group of patient's enrolled in aclinical trial, e.g., with the patient's further being grouped based onone or more arms of the trial for example a treatment vs. control arm.Another type of cohort analysis may be for a set of subjects for whichground truth or references exist, and this type of cohort may be furtherdecomposed into a training set or “development” set and a test or“holdout” set. Development sets may be supported so as to train 112 thealgorithms and models within analyzer module 120, and holdout sets maybe supported so as to evaluate/validate 113 the performance of thealgorithms or models within analyzer module 120.

With continued reference to FIG. 1, the trainer module 110 may beutilized to train 112 the algorithms and models within analyzer module120. In particular, the trainer module 110, may rely on ground truth 111and/or reference annotations 114 so as to derive weights or models,e.g., according to established machine learning paradigms or byinforming algorithm developers. In example embodiments, classificationand regression models are employed which may be highly adaptable, e.g.,capable of uncovering complex relationships among the predictors and theresponse. However, their ability to adapt to the underlying structurewithin the existing data can enable the models to find patterns that arenot reproducible for another sample of subjects.

Adapting to irreproducible structures within the existing data iscommonly known as model over-fitting. To avoid building an over-fitmodel, a systematic approach may be applied that prevents a model fromfinding spurious structure and enable the end-user to have confidencethat the final model will predict new samples with a similar degree ofaccuracy on the set of data for which the model was evaluated.

Successive training sets may be utilized to determine optimal tuningparameter(s), and a test set may be utilized to estimate an algorithm'sor model's predictive performance. Training sets may be used fortraining each of the classifiers via randomized cross-validation.Datasets may be repeatedly split into training and testing sets and maybe used to determine classification performance and model parameters.The splitting of the datasets into training and test sets occurs using astratified or maximum dissimilarity approaches. In example embodiments are-sampling approach (e.g. bootstrapping) may be utilized within thetraining set in order to obtain confidence intervals for (i) the optimalparameter estimate values, and (ii) the predictive performance of themodels.

FIG. 2 outlines a re-sampling based model building approach 200 whichmay be utilized by the systems and methods of the present disclosure.First, at step 210, a tuning parameter set may be defined. Next, at step220, for each tuning parameter set data is resampled the model is fittedand hold-out samples are predicted. At step 230, Resampling estimatesare combined into a performance profile. Next, at step 240, final tuningparameters may be determined. Finally, at step 250, the entire trainingset is re-fitted with the final tuning parameters. After each model hasbeen tuned from the training set, each may be evaluated for predictiveperformance on the test set. Test set evaluation occur once for eachmodel to ensure that the model building process does not over-fit thetest set. For each model that is constructed, the optimal tuningparameter estimates, the re-sampled training set performance, as well asthe test set performance may be reported. The values of the modelparameters over randomized splits are then be compared to evaluate modelstability and robustness to training data.

According to the systems and methods of the present disclosure, a numberof models may be tuned for each of the biological properties/analytes(e.g., tissue types) represented in ground truth maps. Model responsesmay include, for example, covariance based techniques, non-covariancebased techniques, and tree based models. Depending on theirconstruction, endpoints may have continuous and categorical responses;some of the techniques in the above categories are used for bothcategorical and continuous responses, while others are specific toeither categorical or continuous responses. Optimal tuning parameterestimates, the re-sampled training set performance, as well as the testset performance may be reported for each model.

As model complexity grows, predictive performance often follows. Thiscomes at the expense model interpretability. The parameter coefficientsfrom a multiple linear regression model intuitively link each predictorto the response. The same kind of interpretation cannot be uncovered ina neural network, support vector machine, or many of the other models.However, these models may provide much better predictive ability,especially if the underlying relationship between the predictors and theresponse is non-linear. To tease out some interpretive information,variable importance calculations are performed. The main idea behindvariable importance projection methods is that these techniques providea weight to the individual features based on the extent that theycontribute to a low dimensional data representation. For instance forproblems where the number of features is equal to or larger than thenumber of training instances, classifier models can be subject to the“curse of dimensionality” problem. Techniques developed in conjunctionwith Principal component analysis (a linear dimensionality reductionmethod) to understand which predictors are most important for theunderlying model and can direct the user to scientific connectionsbetween the predictors and the response.

TABLE 1 Delineate Field Register multiple data streams across a fieldSegment organs, vessels, lesions, and other application-specific objectsReformat anatomy for specific analyses Delineate Target Registermultiple data streams at a locale Fine-grained segmentation Measure sizeand/or other relevant anatomic structure Extract whole-target featuresDelineate Sub-target Split target into sub-targets according to regionsapplication Sub-target specific calculations Delineate Components (Re-)Segment Component Calculate Readings Visualize Probability Map DetermineDisease Severity Determine Phenotype Predict Outcome Compare Multiple(Optional) Compare Multiple Timepoints Timepoints Assess multi-focaldisease Aggregate across target lesions over a wide scan field. GeneratePatient Report Generate Patient Report

Table 1, above, provides a summary of some of the examplefunctionalities of the analyzer module 120 of system 100. Namely, theanalyzer module 120 may be configured to delineate fields, for example,to register multiple data streams across a field; to segment organs,vessels, lesions and other application-specific objects; and/or toreformat/reconfigure anatomy for specific analyses. The analyzer module120 may further be configured for delineating a target, for example, alesion, in a delineated field. Delineating a target may, for example,include registering multiple data streams at a locale; conductingfine-grained segmentation; measuring size and/or other characteristicsof relevant anatomic structures; and/or extracting whole-target features(e.g., biological properties/analytes characteristic of the entiretarget region). In some embodiments, one or more sub-target regions mayalso be delineated, for example, a target region may be split intosub-targets according to a particular application with sub-targetspecific calculations (e.g., biological properties/analytescharacteristic of a sub-target region). The analyzer module 120 may alsodelineate components or relevant features (such as composition), forexample, in a particular field, target or sub-target region. This mayinclude segmenting or re-segmenting the components/features, calculatingvalues for the segmented components/features (e.g., biologicalproperties/analytes characteristic of the component/feature) andassigning a probability map to the readings. Next pathologies may bedetermined, based on the biological quantified properties/analytes, andcharacterized, e.g., by determining phenotype and/or predictive outcomesfor the pathologies. In some embodiments, the analyzer module 120 may beconfigured to compare data across multiple timepoints, e.g., one or moreof the biological components/analytes may involve a time basedquantification. In further embodiments, a wide scan field may beutilized to assess multi-focal pathologies, e.g., based on aggregatequantifications of biological properties/analytes across a plurality oftargets in the delineated field. Finally, based on the forgoinganalytics, the analyzer module 120 may be configured to generate apatient report.

A sample patient report 300 is depicted in FIG. 3. As shown, the samplepatient report 300 may include quantifications of biologicalparameters/analytes such as relating to structure 310 and composition320 as well as data from non-imaging sources such as hemodynamics 330.The sample patient report may further include visualizations 340, e.g.,2D and/or 3D visualizations of imaging data as well as combinedvisualizations of non-imaging data such as hemodynamic data overlaidonto imaging data. Various analytics 350 may be displayed for assessingthe biological parameters/analytes including, e.g., a visualization ofone or more model(s) (e.g., a decision tree model) fordetermining/characterizing pathology. Patient background and identifyinginformation may further be included. Thus, the analyzer module 120 ofsystem 100 may advantageously provide a user, e.g., a clinician withcomprehensive feedback for assessing the patient.

Advantageously the systems and methods of the present disclosure may beadapted for specific applications. Example vascular and lungapplications are described in greater detail in the sections whichfollow (although it will be appreciated that the specific applicationdescribed have general implications and interoperability with respect tonumerous other applications). Table 2 provides an overview of vascularand lung related applications utilizing a hierarchical analyticsframework as described herein.

TABLE 2 Vascular Application Lung Application Modality CT or MR CTIndication Asymptomatic CAS Lung Cancer Screening Cryptogenic strokeDrug therapy response NSTEMI, CABG Patency assessment EvaluationCompanion-diagnostic for Companion-diagnostic for expensive or targeteddrugs expensive or targeted drugs Diseases Peripheral and coronaryartery Lung cancer first, then other vasculopathy pulmonary diseaseBiological Structure Size, Shape/Margin Properties Composition Solidity,Heterogeneity Hemodynamics Invasive Potential Gene Expression CorrelatesGene Expression Correlates Extension Ultrasound and/or PET and/ormulti-energy CT multi-energy CT

The following sections provide specific examples of quantitativebiological properties/analytes that may be utilized by the systems andmethods of the present disclosure with respect to vascular applications:

Anatomic Structure:

Vessel structural measurements, specifically those that lead to thedetermination of % stenosis, have long been and remain the single mostused measurements in patient care. These were initially limited to innerlumen measurements, rather than wall measurements involving both theinner and outer surfaces of the vessel wall. However, all of the majornon-invasive modalities, unlike X-ray angiography, can resolve thevessel wall and with this come expanded measurements that may beachieved. The category is broad and the measurements are of objects ofvarying sizes, so generalizations should be made with care. A primaryconsideration is the limit of spatial sampling or resolution. Theminimally detectable changes in wall thickness may, however, be lowerthan the spatial sampling by taking advantage of subtle variations inintensity levels due to partial volume effect. Additionally, statedresolutions generally refer to grid size and field of view ofpost-acquisition reconstructions rather than the actual resolving powerof the imaging protocol, which determines the minimum feature size thatcan be resolved. Likewise, in-plane vs. through-plane resolutions may ormay not be the same and not only the size of a given feature but as wellits proportions and shape will drive the measurement accuracy. Last butnot least, in some cases categorical conclusions are drawn from applyingthresholds to the measurements, which may then be interpreted accordingto signal detection theory with the ability to optimize the trade-offbetween sensitivity and specificity, terms that don't otherwise refer tomeasurements in the normal sense.

Tissue Characteristics:

The quantitative assessment of the individual constituent components ofthe atherosclerotic plaques, including lipid rich necrotic core (LRNC),fibrosis, intraplaque hemorrhage, permeability, and calcification, canprovide crucial information concerning the relative structural integrityof the plaque that could aid the physician's decisions on course ofmedical or surgical therapy. From the imaging technology point of view,the ability to do this lies less with spatial resolution as withcontrast resolution and tissue discrimination made possible by differingtissues responding to incident energy differently so as to produce adiffering receive signal. Each imaging modality does this to someextent; terms in ultrasound such as “echolucency”, the CT number inHounsfield Units, and differentiated MR intensities as a function ofvarious sequences such as (but not limited to) T1, T2 and T2*.

Dynamic Tissue Behavior (e.g., Permeability):

In addition to morphological features of the vessel wall/plaque, thereis increasing recognition that dynamic features are valuablequantitative indicators of vessel pathology. Dynamic sequences where theacquisition is taken at multiple closely-spaced times (known as phases)expand the repertoire beyond spatially-resolved values t includetemporally-resolved values which may be used for compartment modeling orother techniques to determine the tissues' dynamic response to stimulus(such as but not limited to wash-in and wash-out of contrast agent).Through the use of dynamic contrast enhanced imaging with ultrasound orMR in the carotid arteries or delayed contrast enhancement in thecoronary arteries, sensitive assessments of the relative permeability(e.g., Ktrans and Vp parameters from kinetic analysis) of themicrovascular networks of neoangiogenesis within the plaques of interestcan be determined. In addition, these dynamic series can also aid in thedifferentiation between increased vascular permeability versusintraplaque hemorrhage.

Hemodynamics:

The basic hemodynamic parameters of the circulation have a direct effecton the vasculopathy. Blood pressures, blood flow velocity, fractionalflow reserve (FFR) and vessel wall shear stress may be measured bytechniques ranging from very simple oscillometry to sophisticatedimaging analysis. Using common principles of fluid dynamics,calculations of vessel wall shear stress can be ascertained fordifferent regions of the wall. In similar fashion MRI, with or withoutthe combination of US, has been used to calculate the wall shear stress(WSS) and correlate the results with structural changes in the vessel ofinterest. In addition, the effects of antihypertensive drugs onhemodynamics have been followed for short and long-term studies.

Thus, in example embodiments, key aspects of applying the systems andmethods of the present disclosure in a vascular setting may includeevaluating plaque structure and plaque composition. Evaluating plaquestructure may advantageously include, e.g., lumen measurements (whichimproves stenosis measurement by providing area rather than onlydiameter measures) as well as wall measurements (e.g., wall thicknessand vascular remodeling). Evaluating plaque composition mayadvantageously involve quantification of tissue characteristics (e.g.,lipid core, fibrosis, calcification, and permeability) rather than just“soft” or “hard” designations as typically found in the prior art.Tables 3 and 4, below, describe example structural calculations andtissue characteristic calculations, respectively which may be utilizedby the vascular applications of the systems and methods of the presentdisclosure.

TABLE 3 Structural calculations of vessel anatomy supported by vascularapplications of the systems and methods disclosed herein. MeasurandDescription Type and Units Remodeling Calculated as the ratio of vesselarea with Expressed with value less than 1 Ratio plaque to referencevessel wall area for inward remodeling and without plaque greater than 1for outward remodeling % Stenosis Calculated as the (1 − ratio ofminimum Expressed as percentage >0% lumen with plaque to reference lumenwithout plaque) × 100 both by area and by diameter % Dilation Calculatedas the (ratio of maximum Expressed as percentage >0% lumen with plaqueto reference lumen without plaque − 1) × 100 both by area and diameterWall Calculated by measuring the largest Expressed in units of mmThickness thickness of wall

TABLE 4 Calculations of tissue characteristics supported by vascularapplications of the systems and methods disclosed herein MeasurandDescription Type and Units Lipid Core The pathologic retention oflipids, particularly Burden in mm² by lipoproteins, by intimal/medialcells leading to cross section and mm³ progressive cell loss, celldeath, degeneration, and by target and vessel necrosis. It is a mixtureof lipid, cellular debris, blood and water in various concentrations.Fibrosis The pathologic and sometimes physiologic defensive Burden inmm² by production of fibrous tissue by fibroblasts and cross section andmm³ activated smooth muscle cells. by target and vessel CalcificationThe physiologic defensive biological process of Agatston score andattempting to stabilize plaque, which has a burden in mm² by mechanismakin to bone formation. cross section and mm³ by target and vesselHemorrhage A pathologic component that may contribute to the Burden inmm² by vulnerability of a plaque. Its role is not fully cross sectionand mm³ understood, but it is believed to be a driving force in bytarget and vessel plaque progression through lipid accumulation from redblood cells. Permeability Described as endothelial and intimalpermeability due Burden in mm² by to neovascularization, necrosis,collagen breakdown, cross section and mm³ and inflammation by target andvessel Thrombosis Local coagulation or clotting of the blood in a partof Degree the circulatory system. Ulceration Disintegration and necrosisof epithelial tissue Burden in mm² by cross section and mm³ by targetand vessel

Example systems relating to evaluating the vascular system mayadvantageously include/employ algorithms for evaluating vascularstructure. Thus, the systems may employ, e.g., a target/vesselsegment/cross-section model for segmenting the underlying structure ofan imaged vessel. Advantageously a fast marching competition filter maybe applied to separate vessel segments. The systems may further beconfigured to handle vessel bifurcations. Image registrations may beapplied utilizing Mattes mutual information (MR) or mean square error(CT) metric, rigid versor transform, LBFGSB optimizer, or the like. Asnoted herein, vessel segmentation may advantageously include lumensegmentation. An initial lumen segmentation may utilize a confidenceconnected filter (e.g., carotid, vertebral, femoral, etc.) todistinguish the lumen. Lumen segmentation may utilize MR imaging (suchas a combination of normalized, e.g., inverted for dark contrast,images) or CT Data (such as use of registered pre-contrast,post-contrast CT and 2D Gaussian distributions) to define a lumennessfunction. Various connected components may be analyzed and thresholdingmay be applied. Vessel segmentation may further entail outer wallsegmentation (e.g., utilizing a minimum curvature (k2) flow to accountfor lumen irregularities). In some embodiments, an edge potential map iscalculated as outward-downward gradients in both contrast andnon-contrast. In example embodiments, outer wall segmentation mayutilize cumulative distribution functions (incorporating priordistributions of wall thickness, e.g., from 1-2 adjoining levels) in aspeed function to allow for median thickness in the absence of any otheredge information. In example embodiments, ferret diameters may beemployed for vessel characterization. In further embodiments, wallthickness may be calculated as the sum of the distance to lumen plus thedistance to the outer wall.

Example systems relating to evaluating the vascular system may furtheradvantageously analyze vascular composition. For example, in someembodiments, composition may be determined based on image intensity andother image features. In some embodiments, the lumen shape may beutilized, e.g., as relating to determining thrombosis. Advantageously,an analyte blob model may be employed for better analyzing compositionof particular sub-regions of the vessel. We define an analyte blob to bea spatially contiguous region, in 2D, 3D, or 4D images, of one class ofbiological analyte. The blob model may utilize an anatomically alignedcoordinate system using isocontours, e.g., in normalized radial distancefrom the lumen surface to the adventitial surface of the vessel wall.The model may advantageously identify one or more blobs and analyze eachblobs location e.g., with respect to the overall vessel structure aswell as relative to other blobs. In example embodiments, a hybridBayesian/Markovian network may be utilized to model a relative locationof a blob. The model may advantageously account for the observed imageintensity at a pixel or voxel being influenced by a local neighborhoodof hidden analyte category nodes thereby accounting for partial volumeand scanner point spread function (PSF). The model may further allow fordynamically delineating analyte blob boundaries from analyte probabilitymaps during inference by the analyzer module. This is a key distinctionfrom typical machine vision approaches, such as with superpixelapproaches, that pre-compute small regions to be analyzed but are unableto dynamically adjust these regions. An iterative inference proceduremay be applied that utilizes uses the current estimate of both analyteprobability and blob boundaries. In some embodiments parametric modelingassumptions or kernel density estimation methods may be used to enableprobability density estimates between the sparse data used to train themodel.

Introduced herein is a novel model for classification of composition ofvascular plaque components that removes the requirements forhistology-to-radiology registration. This model still utilizesexpert-annotated histology as a reference standard but the training ofthe model does not require registration to radiological imaging. Themulti-scale model computes the statistics of each contiguous region of agiven analyte type, which may be referred to as a ‘blob’. Within across-section through the vessel, the wall is defined by two boundaries,the inner boundary with the lumen and the outer boundary of the vesselwall, creating a donut shape in cross section.

Within the donut shaped wall region, there are a discrete number ofblobs (different than the default background class of normal wall tissuewhich is not considered to be a blob). The number of blobs is modeled asa discrete random variable. Then, each blob is assigned a label ofanalyte type and various shape descriptors are computed. Additionally,blobs are considered pairwise. Finally, within each blob, each pixel canproduce a radiological imaging intensity value, which are modeled asindependent and identically distributed (i.i.d.) samples that come froma continuously valued distribution specific to each analyte type. Notethat in this last step, the parameters of the imaging intensitydistributions are not part of the training process.

One key feature of this model is that it accounts for the spatialrelationship of analyte blobs within the vessel and also to each other,recognizing that point-wise image features (whether from histologyand/or radiology) is not the only source of information for experts todetermine plaque composition. While the model allows for the ability totrain without explicit histology-to-radiology registration, it couldalso be applied in situations where that registration is known. It isbelieved that statistically modeling the spatial layout ofatherosclerotic plaque components for classifying unseen plaques is anovel concept.

Example techniques for estimating vessel wall composition from CT or MRimages are further elaborated on in the following section. Inparticular, the methods may employ a multi-scale Bayesian analyticmodel. The basic Bayesian formulation is as follows:

${P\left( A \middle| I \right)} = {{\frac{{P\left( I \middle| A \right)} \cdot {P(A)}}{P(I)}\mspace{14mu} {posterior}} = \frac{{likelihood} \cdot {prior}}{evidence}}$

In the context of the present disclosure, the hypothesis may be based ona multi-scale vessel wall analyte map, A, with observation combing fromCT or MR image intensity information I.

As depicted in FIG. 4, the multi-scale vessel wall analyte map mayadvantageously include wall-level segmentation 410 (e.g., across-sectional slice of the vessel), blob-level segmentation andpixel-level segmentation 430 (e.g., based on individual image pixels.E.g., A=(B,C) may be defined as a map of vessel wall class labels(similar to a graph with vertices B and edges C), wherein B is a set ofblobs (cross-sectionally contiguous regions of non-background wallsharing a label) and C is a set of blob couples or pairs. B_(b) may bedefined as a generic single blob where bε[1 . . . n_(B)] is an index ofall blobs in A. B_(b) ^(a) is a blob with label a. For statisticalpurposes, the individual blob descriptor operator D_(B){ } is in anlow-dimensional space. C_(c) may be defined as a blob pair where cε[1 .. . n_(B)(n_(B)−1)/2] is an index of all blob pairs in A. C_(c) ^(f,g)is a blob pair with labels f and g. For statistical purposes, the blobpair descriptor operator D_(C){ } is in a low-dimensional space. A(x)=amay be defined as the class label of pixel x where aε{‘CALC’, ‘LRNC’, ‘FIBR’, ‘IPH’, ‘background’} (compositional characteristics). Inexemplary embodiments, I(x) is the continuous valued pixel intensity atpixel x. Within each blob, I(x) are modeled as independent. Note thatbecause the model is used to classify wall composition in 3Dradiological images, the word “pixel” is used to generically denote both2D pixels and 3D voxels

Characteristics of blob regions of like composition/structure mayadvantageously provide insights regarding the disease process. Eachslice (e.g., cross-sectional slice) of a vessel may advantageouslyinclude a plurality of blobs. Relationships between blobs may beevaluated in a pairwise manner. The number of blobs within across-section is modeled as a discrete random variable and may also beof quantifiable significance. At the slice-level of segmentation,relevant characteristics (e.g., biological properties/analytes) mayinclude a quantification of a total number of blobs and/or a number ofblobs of a particular structure/composition classification;relationships between the blobs, e.g., spatial relationships such asbeing closer to the interior. At the blob level of segmentation,characteristics of each blob, such as structural characteristics, e.g.,size and shape, as well as compositional characteristics, etc., may beevaluated serving as a biological properties/analytes. Finally at apixel-level of segmentation, individual pixel level analysis may beperformed, e.g., based image intensity distribution.

Probability mapping of characteristics may be applied with respect tothe multi-scale vessel wall analyte map depicted in FIG. 4. Theprobability mapping may advantageously establish a vector ofprobabilities for every pixel with components of the vector for theprobability of a each class of analyte and one component for theprobability of background tissue. In example embodiments, sets ofprobability vectors may represent mutually exclusive characteristics.Thus, each set of probability vectors representing mutually exclusivecharacteristics will sum to 1. For example, in some embodiments, it maybe known that a pixel should fall into one and only one compositionalcategory (e.g., a single coordinate of a vessel cannot be both fibrousand calcified). Of particular note, the probability mapping does notassume independence of analyte class between pixels. This, is becauseneighboring pixels or pixels within a same blob may typically have sameor similar characteristics. Thus, the probability mapping accounts, asdescribed in greater detail herein, advantageously accounts fordependence between pixels.

f(A=α) may be defined as the probability density of map α. f(A) is theprobability distribution function over all vessel walls.f(D_(B){B^(a)}=β) is the probability density of descriptor vector β withlabel a. f(D_(B){B^(a)}) is the probability density function (pdf) ofblob descriptors with label a. There is a probability distributionfunction for each value of a. f(B)=Πf(D_(B){B^(a)}) f(D_(C){C^(f,g)}=γ)is the probability density of pairwise descriptor vector γ with labels fand g. f(D_(c){C^(f,g)}) is the probability density function (pdf) ofpairwise blob descriptors. There is a probability distribution functionfor each ordered pair f,g. Thus:

f(C)=Π(D _(c) {C ^(a)})

f(A)=f(B)f(C)=Πf(D _(b) {B ^(a)})Πf(D _(c) {C ^(a)})

P(A(x)=a) is the probability of pixel x having label a. P(A(x)) is theprobability mass function (pmf) of analytes (prevalence). It can beconsidered a vector of probabilities at a specific pixel x or as aprobability map for a specific class label value.

Note that:f(A)=P(N)·f(C)·f(B)=P(N)·Πf(C _(c))·Πf(B _(b))

f(C_(c)=γ) is the probability density of pairwise descriptor vector γ.J(C_(c)) is the probability density function (pdf) of pairwise blobdescriptors. f(B_(b)=β) is the probability density of descriptor vectorβ. f(B_(b)) is the probability density function (pdf) of blobdescriptors. P(A(x)=a) is the probability of pixel x having label a.P(A(x)) is the probability mass function (pmf) of analytes (prevalencein a given map). It can be considered a vector of probabilities at aspecific pixel x or as a spatial probability map for a specific analytetype. P(A(x)=a|I(x)=i) is the probability of analyte given the imageintensity that is our main goal to compute. P(I(x)=i|A(x)=a) is thedistribution of image intensities for a given analyte.

FIG. 5 depicts an exemplary pixel-level probability mass function as aset of analyte probability vectors. As noted above, the followingassumptions may inform the probability mass function: Completeness: inexample embodiments one may assume a sufficiently small pixel must fallinto at least one of the analyte classes (including a catch-all‘background’ category) and thus the sum of probabilities sums to 1.Mutual exclusivity: a sufficiently small pixel may be assumed to belongto only one class of analyte; if there are combinations (i.e.,spiculated calcium on LRNC background), then a new combination class canbe created in order to retain mutual exclusivity. Non-independence: eachpixel may be assumed to be highly dependent on its neighbors and theoverall structure of A.

An alternative view of the analyte map is as a spatial map ofprobability for a given analyte. At any given point during inference,analyte blobs can be defined using the full width half max rule. Usingthis rule, for each local maxima of probability for that analyte aregion is grown outward to a lower threshold of half the local maximavalue. Note that this 50% value is a tunable parameter. Spatialregularization of blobs can be done here by performing some curvatureevolution on probability maps in order to keep boundaries more realistic(smooth with few topological holes). Note that different possibleputative blobs of different analyte classes may in general have spatialoverlap because until one collapses the probabilities these representalternative hypotheses for the same pixel and hence the modifier‘putative’.

When iterative inference is terminated, there are several options forpresentation of the results. First, the continuously valued probabilitymaps can be presented directly to the user in one of several formsincluding but not limited to surface plots, iso-contour plots, or usingimage fusion similar to visualizing PET values as variation in hue andsaturation on top of CT. A second alternative is to collapse theprobability map at each pixel by choosing a single analyte label foreach pixel. This can be done most straightforwardly by choosing themaximum a posteriori value at each pixel independently, thus creating acategorical map which could be visualized by assigning a distinct colorto each analyte label and assigning either full or partial opacity ontop of the radiological image. Under this second alternative, the labelvalues might be assigned non-independently by resolving overlappingputative blobs based on a priority the probability of each blob. Hence,at a given pixel a lower priority analyte probability might be used forthe label if it belongs to a higher probability blob.

FIG. 6 illustrates a technique for computing putative analyte blobs. Inexample embodiments putative blobs may have overlapping regions. Thus,it may be advantageous to apply analytical techniques to segmentingpixels by putative blobs. For a probability of a given analyte the localmaxima in probability is determined. The full width half max rule maythen be applied to determine discrete blobs. At any given iteration ofinference, analyte blobs can be defined using the full width half maxrule. Find local maxima, then region grow with a lower threshold of0.5*max. (The 50% value could be a tunable parameter.) In someembodiments, spatial regularization of blobs may also be applied, e.g.,by performing some curvature evolution on probability maps in order tokeep boundaries smooth and avoid holes. Note that at this stagedifferent possible putative blobs of different analyte classes may, ingeneral, have spatial overlap because until probabilities are collapsedthese represent alternative hypotheses. Thus, an image-level analyte mapbe computed, e.g., based on a collapse of the probability map function.Notably, this collapse can be determined based on either the pixel-levelanalyte probability map, the putative blobs or a combination of both.With respect to the pixel-level analyte probability map, collapse can bedetermined by for each pixel, by choosing the label with maximumprobability A(x):=arg maxa P(A(x)=a). This is similar to implementationViterbi algorithm. Basically the highest probability for each set ofmutually exclusive probabilities vectors is locked in (e.g. with analytepriorities breaking possible ties). All other probabilities in the setmay then be set to zero. In some embodiments, probabilities forneighboring pixels/regions may be taken into account when collapsingdata on a pixel level. With respect to putative blob level collapse,overlapping putative blobs may be resolved. In some embodiments,prioritization can be based on blob probability density f(D₁{A_(a)^(b)}=d₁). Since higher probability blobs may change shape of overlappedlower probability blob this may impact analysis of blob levelcharacteristics. In example embodiments, the full range of probabilitiesmay be maintained rather than collapsing the data.

In order to model the relative spatial positioning of blobs within thevessel wall, an appropriate coordinate system can be chosen in order toprovide rotational-, translational-, and scale-invariance betweendifferent images. These invariances are important to the model becausethey allow the ability to train on one type of vessel (e.g., carotidswhere endarterectomy specimens are easily available) and apply the modelto other vessel beds (e.g., coronary where plaque specimens aregenerally not available) under the assumption that the atheroscleroticprocess is similar across different vessel beds. For tubular objects, anatural coordinate system follows from the vessel centerline wheredistance along the centerline provides a longitudinal coordinate andeach plane perpendicular to the centerline has polar coordinates ofradial distance and angle. However, due to the variability of vesselwall geometry, especially in the diseased patients, which one may aim toanalyze, an improved coordinate system may be utilized. The longitudinaldistance is computed in a way so that each 3D radiological image pixelis given a value, not just along the centerline or along interpolatedperpendicular planes. For a given plaque, the proximal and distal planesperpendicular to the centerline are each used to create an unsigneddistance map on the original image grid, denoted P(x) and D(x),respectively where x represents the 3D coordinates. The distance mapl(x)=P(x)/(P(x)+D(x)), represents the relative distance along the plaquewith a value of 0 at the proximal plane and 1 at the distal plane. Thedirection of the l-axis is determined by ∇l(x).

Because the geometry of the wall may be significantly non-circular, theradial distance may be defined based on the shortest distance to theinner luminal surface and the shortest distance to the outer adventitialsurface. The expert-annotation of the histology images includes regionsthat define the lumen and the vessel (defined as the union of the lumenand vessel wall). A signed distance function can be created for each ofthese, L(x) and V(x), respectively. The convention is that the interiorof these regions is negative so that in the wall L is positive and V isnegative. The relative radial distance is computed as r(x)=L(x)/(L(x)−V(x)). It has a value of 0 at the luminal surface and 1at the adventitial surface. The direction of the r-axis is determined by∇r(x).

Because of the non-circular wall geometry, the normalized tangentialdistance may be defined as lying along iso-contours of r (and of l ifprocessing in 3D). The direction of the t-axis is determined by ∇r×Δl.The convention is that histology slices are assumed to be viewed lookingfrom the proximal to the distal direction so that positive l points intothe image. Note that unlike the others, t does not have a natural originsince it wraps onto itself around the vessel. Thus, one can define theorigin of this coordinate differently for each blob relative to thecentroid of the blob.

Another wall coordinate that is used is normalized wall thickness. Insome sense, this is a proxy for disease progression. Thicker wall isassumed to be due to more advanced disease. Assumption that statisticalrelationship of analytes changes with more advanced disease. Theabsolute wall thickness is easily calculated as w_(abs)(x)=L(x)−V(x). Inorder to normalize it to the range of [0-1], one may determine thatmaximum possible wall thickness when the lumen approaches zero size andis completely eccentric and near the outer surface. In this case themaximum diameter is the maximum Feret diameter of the vessel, D_(max).Thus the relative wall thickness is computed as w(x)=w_(abs)(x)/D_(max).

The degree to which the aforementioned coordinates may or may not beused in the model is in part dependent on the amount of training dataavailable. When training data is limited, several options are available.The relative longitudinal distance may be ignored treating differentsections through each plaque as though they come from the samestatistical distribution. It has been observed that plaque compositionchanges along the longitudinal axis with more severe plaque appearancein the middle. However, instead of parameterizing the distributions byl(x), this dimension can be collapsed. Similarly, the relative wallthickness may also be collapsed. Observations have been made thatcertain analytes occur in “shoulder” regions of plaques where w(x) wouldhave a middle value. However, this dimension can also be collapsed untilenough training data is available.

As noted above, a vessel wall composition model may be utilized as theinitial hypothesis (e.g., at the prior P(A)). FIG. 7 depicts normalizedvessel wall coordinates for an exemplary vessel wall composition model.In the depicted model, l is relative longitudinal distance along vesseltarget from proximal to distal, which may be calculated, e.g., on anormalized the interval [0,1]. The longitudinal distance may be computedwith 2 fast marching propagations starting from proximal and from distalplanes to compute unsigned distance fields P and D wherein l=P/(P+D).l-axis direction is ∇l. As depicted, r is normalized radial distancewhich may also be calculated on a normalized interval [0,1] from luminalto adventitial surface. Thus, r=L/(L+(−V)) where L is lumen signeddistance field (SDF) and Vis vessel SDF. r-axis direction is ∇r.Finally, t is normalized tangential distance which may be computed,e.g., on a normalized interval [−0.5,0.5]. Notably, in exampleembodiments there is may be no meaningful origin for the entire wall,only for individual analyte blobs (thus, t origin may be at blobcentroid). The tangential distance is computed along iso-contour curvesof l and of r. t-axis direction is ∇r×∇l.

FIG. 9 illustrates some complex vessel topologies which can be accountedfor using the techniques described herein. In particular, whenprocessing CT or MR in 3D, different branches may be advantageouslyanalyzed separately so that the relationship between analyte blobs inseparate branches are properly ignored. Thus, if a segmented view(cross-sectional slice) If includes more than one lumen, one can accountfor this by performing a watershed transform on r in order to split upwall into domains belonging to each lumen after which each domain may beseparately considered/analyzed.

As noted above, many of the coordinates and probability measurementsdescribed herein may be represented utilizing normalized scales therebypreserving scale invariance, e.g., between different sized vessels.Thus, the proposed model may advantageously be independent of absolutevessel size, under the assumption that a disease process is similar andproportional for different caliber vessels.

In some embodiments, the model may be configured to characterizeconcentric vs. eccentric plaque. Notably, a normalized all thicknessclose to 1 may indicate highly eccentric place. In further embodiments,inward vs. outward plaque characterization may be implemented. Notably,histological information on this characteristic is hindered bydeformation. Thus, in some embodiments, CT and training data may beutilized to establish an algorithm for determining inward vs. outwardplaque characterization.

As noted above, in example embodiments, non-imaging data, such ashistology data, may be utilized as a training set for establishingalgorithms linking image features to biological properties/analytes.There are however, some differences between the data types that need tobe addressed in ensuring a proper correlation. For example, thefollowing differences between histology and imaging may impact propercorrelation: Carotid endarterectomy (CEA) leaves adventitia and somemedia behind in patient CT or MR image analysis presumed to find outeradventitial surface. (See e.g., FIG. 8 depicting the margin between theplaque removed for the histology specimen relative to the outer vesselwall). Notably, scientific literature shows uncertainty of whethercalcification can occur in adventitia. The following techniques may beemployed to account for this difference. Histology can be dilatedoutward, e.g., based on an assumption that little to no analyte in thewall is left behind. Alternatively, Image segmentation can be erodedinward, e.g., based on knowledge of typical or particular margins left.For example, an average margin may be utilized. In some embodiment anaverage margin may be normalized a percentage of the overall diameter ofthe vessel. In further embodiments, histology may be used to mask theimaging (e.g., overlay, based on alignment criteria). In suchembodiments it may be necessary to apply one or more transformations tothe histology data to match proper alignment. Finally, in someembodiments, the difference may be ignored (which is equivalent touniform scaling of removed plaque to entire wall). While this may inducesome small error, presumably the wall left behind may be thin comparedto plaque in CEA patients.

Longitudinal differences may also exist between histological data (e.g.,a training set) and the imaging data as represented by the vessel wallcomposition model. In example embodiments, longitudinal distance may bemodeled/correlated explicitly. Thus, e.g., histology slice numbering(A-G for example) can be used to roughly determine position withinexcised portion of plaque. This approach, however, limits analysis withrespect to other slices without corresponding histology data. Thus,alternatively, in some embodiments, all histology slices may be treatedas arising from the same distribution. In example embodiments, somelimited regularization may still be employed along the longitudinaldirection.

As noted above, normalized wall thickness, in some sense is an imperfectproxy for disease progression. In particular, a thicker wall is assumedto be due to more advanced disease, e.g. based on an assumption thatstatistical relationship of analytes changes with more advanced disease.Normalized wall thickness may be calculated as follows: An absolute wallthickness T_(a) may be determined (in mm), e.g., computed asT_(a)=L+(−V) where L is lumen SDF, Vis vessel SDF and D_(max) is maximumFeret diameter of vessel (in mm). A relative wall thickness T may thenbe computed based on T=T_(a)/D_(max), e.g., on an interval [0,1], where1 indicates thickest part of small lumen indicative of completelyeccentric plaque. In example embodiments, probabilities may beconditioned based on wall thickness, e.g., so that the distribution ofanalyte blobs would depend on wall thickness. This advantageously maymodel differences in analyte composition over the course of diseaseprogression.

FIG. 10 depicts representing an exemplary analyte blob with adistribution of normalized vessel wall coordinates. In particular, theorigin oft is placed at blob centroid. (r,t) coordinates are a randomvector where the location/shape is fully represented by the jointdistribution of points within. This can be simplified by considering themarginal distributions (since radial and tangential shapecharacteristics seem relatively independent). Marginal distributions maybe calculated as projections along r and t (note that l and Tcoordinates can also be considered). Notably, the marginal distributionin the radial direction may advantageously represent/characterize theplaque growth in concentric layers (e.g., medial layer, adventitiallayer and intima layer.) Similarly, the marginal distribution in thetangential direction may advantageously represent a growth factor whichmay be indicative of the staging of the disease. In example embodiments,analyte blob descriptors can be computed based on the marginaldistributions. For example, on can take low order statistics on themarginal distributions (or use histograms or fit parametric probabilitydistribution functions).

In example embodiments, the following analyte blob descriptors may beused, e.g., to capture location, shape or other structuralcharacteristics of individual blobs:

-   -   Location in normalized vessel coordinates        -   Mostly with respect to r            -   e.g., in order to distinguish between shallow/deep                calcification        -   t-direction ignored; [optionally model l-direction]    -   Extent in normalized vessel coordinates        -   Intentionally avoiding the word ‘size’ which implies an            absolute measurement, whereas extent is a normalized value    -   Lopsidedness to represent degree of asymmetry in distribution        -   Clinical significance is unclear but it may help to            regularize shapes against implausible lopsided shapes    -   Alignment to represent confinement to parallel tissue layers        -   Analyte blobs seem to stay within radial layers            (iso-contours of r) quite well so this will help select            image processed shapes that are similar    -   Wall thickness where the blob is located        -   Thick (i.e., advanced) plaques assumed to have different            statistics than thin plaques

In some embodiments, pair-wise blob descriptors may also be utilized.For example:

-   -   Relative location        -   e.g., if fibrosis is on the lumen side of LRNC    -   Relative extent        -   e.g., how thick/wide is fibrosis relative to LRNC    -   Surroundedness        -   How much one marginal projection falls close to the middle            of the other        -   e.g., napkin ring sign or fibrosis around LRNC    -   Relative wall thickness        -   To represent degree of ‘shoulderness’ (shoulder would be            relatively less thick than central plaque body)

It is noted that higher order interactions (e.g., between three blobs orbetween two blobs and another feature), may also be implemented.However, consideration may be given to diminishing returns and traininglimitations.

The following are example quantifications of blob descriptors:

-   -   Individual blob descriptors

Location α_(r) = E[r] Extent β_(r) = Var[r] β_(t) = Var[t] Lopsidednessγ_(r) = |Skewness[r]| γ_(t) = |Skewness[t]| Alignment δ_(r) =Kurtosis[r] δ_(t) = Kurtosis[t] Thickness τ_(T) = E[T]

-   -   Pairwise blob descriptors

Relative location α_(rr) = E[r₂] − E[r₁] α_(tt) = E[t₂] − E[t₁] Relativeextent β_(rr) = Var[r₂]/Var[r₁] β_(tt) = Var[t₂]/Var[t₁] Surroundednessε_(rr) = |α_(rr)|β_(rr) ε_(tt) = |α_(tt)|β_(tt) Relative thicknessτ_(TT) = E[T₂]/E[T₁]

Notably, the set of descriptors (e.g., 8-12 descriptors) form a finiteshape space that a blob lives in. One can then look at the distributionof a population of blobs as a distribution in this finite space. FIG. 11depicts an exemplary distribution of blog descriptors. In exampleembodiments the distribution of blob descriptors may be computed overthe whole training set. In some embodiments, lower order statistics maybe utilized on individual blob descriptors (assuming independence),e.g., Location: E[α_(r)], Var[α_(r)]. In other embodiments, amultidimensional Gaussian (mean vector+covariance matrix) analysis maybe used to model the descriptors (e.g., wherein independence is notassumed). In further embodiments, if the distribution is non-normal itmay be modeled with density estimation techniques.

As noted above, one can also model a number of blobs per cross section(or the number of each class), e.g., η without regard to analyte classand η_(i) counting number in each analyte class. FIG. 14 depictsfrequency distribution of the total number of blobs for each histologyslide. A poison regression is applied as an overly. Note that theanalytic chart of FIG. 14 depicts the number of blobs per cross sectionN without regard to analyte class (number of blobs of each analyte typeis represented by B).

Summarizing the forgoing sections, in example embodiments, the overallvessel wall composition model may include the following:

-   -   Per-pixel analyte prior pmf

P(A(x)=a _(i))=ρ_(i)

-   -   Individual blob descriptors

B ₁=(α_(r),β_(r),β_(t),δ_(r),δ_(t),τ_(T))

B ₁ ˜N(μ₁,Σ₁)

-   -   Pairwise blob descriptors

C ₂=(α_(rr),α_(tt),β_(rr),β_(tt),ε_(rr),ε_(tt),τ_(TT))

C ₂ ˜N(μ₂,Σ₂)

-   -   Number of blobs

η˜Poisson(λ_(n))

-   -   wherein:

P(A(x)=a _(i))=ρ_(i)

f(A ^(b))=f(B ₁ ^(b))

${f(A)} = {{P(\eta)} \cdot \left( {\prod\limits_{b \neq c}{f\left( C_{2}^{bc} \right)}} \right) \cdot {\prod\limits_{b}{f\left( A^{b} \right)}}}$

As noted above, an imaging model may serve as an the likelihood (e.g.,P(I\A)) for the Bayesian analytic model. A maximum likelihood estimatemay then be determined. In example embodiments, this may be doneconsidering each pixel in isolation (e.g., without regard to the priorprobability of the structure in the model). Estimated analyte maps aretypically smooth only because images are smooth (which is why no priorsmoothing is typically performed). Independent pixel-by-pixel analysiscan be done, e.g., at least up to the point of accounting for scannerPSF. The imaging model is utilized to account for imperfect imagingdata. For example, imaging small components of plaque adds independentnoise on top of pixel values. Moreover, the partial volume effect andscanner PSF are well known as applying to small objects. Thus, given amodel (e.g., level set representation of analyte regions), simulating CTby Gaussian blurring with PSF is easy and fast. The imaging modeldescribed herein may also be applied to determine (or estimate) thedistribution of true (not blurred) densities of different analytes.Notably this cannot come from typical imaging studies since these willhave blurred image intensities. In some embodiments, wide variancescould be used to represent the uncertainty. Alternatively, distributionparameters could be optimized from training set but the objectivefunction would have to be based on downstream readings (of analyteareas), e.g., unless aligned histology data is available. FIG. 12depicts the exemplary model for imaging data (e.g., correlating betweena hidden (categorical) state (A(x)) and an observed (continuous) state(I(x)) whereby random (e.g., analyte density distribution (H(A(x))) anddeterministic (e.g., scanner blur *G(x)) noise factors are accountedfor. θ are the parameters of H (proportion & HU mean/variance of eachanalyte). θ=(τ₁, μ₁, σ₁, . . . , τ_(N), μ_(N), σ_(N)) for N differentanalyte classes assuming normal distributions. Note that θ are patientspecific and will be estimated in an expectation maximization (EM)fashion, e.g., wherein analyte labels are the latent variables and theimage is observed data.

E-step: determine membership probabilities given current parameters

-   -   M-step: maximize likelihood of parameters given membership        probabilities

FIG. 13 depicts a diagram of an example Markov model/Viterbi algorithmfor relating an observed state to a hidden state in an image model. Inparticular, the diagram depicts an observed state (gray) (observed imageintensity, I(x)) and a hidden state (white) (pure analyte intensity,H(A(x))) which can be modeled either with empirical histogram or withGaussian or boxcar probability distribution function. PSF of imagingsystem is modeled as Gaussian, G(x). Thus,

I(x)=G(x)*H(A(x))

It is noted that a Viterbi-like algorithm could apply here butconvolution would replace emission probabilities H could be modeled asGaussian or uniform.

As noted above, one portion of the inference procedure is based uponexpectation maximization (EM). In a typical application of EM, datapoints are modeled as belonging to one of several classes, which isunknown. Each data point has a feature vector and for each class, thisfeature vector may be modeled with a parametric distribution such as amultidimensional Gaussian, represented by a mean vector and a covariancematrix. In the context of the model presented herein, a straightforwardEM implementation would work as follows:

$\mspace{20mu} {{L\left( {\theta;I} \right)} = {\prod\limits_{x = 1}^{N_{pixels}}{\sum\limits_{a = 1}^{N_{analystes}}{\tau_{a}{G\left( {{{I(x)};\mu_{a}},\sigma_{a}} \right)}\mspace{14mu} {where}}}}}$  G  is  Gaussian  function $\begin{matrix}{{L\left( {{\theta;I},A} \right)} = {p\left( {I,\left. A \middle| \theta \right.} \right)}} \\{= {\prod\limits_{x = 1}^{N_{pixels}}{\sum\limits_{a = 1}^{N_{analytes}}{\delta_{a,{A{(x)}}}\tau_{a}{G\left( {{{I(x)};\mu_{a}},\sigma_{a}} \right)}}}}} \\{{{where}\mspace{14mu} \delta \mspace{14mu} {is}\mspace{14mu} {Kronecker}\mspace{14mu} {delta}}} \\{= {\exp \left\{ {\sum\limits_{x = 1}^{N_{pixels}}{\sum\limits_{a = 1}^{N_{analytes}}{\delta_{a,{A{(x)}}}\left\lbrack {{\ln \; \tau_{a}} - \frac{\ln \left( {2\pi \; \sigma_{a}^{2}} \right)}{2} - \frac{\left( {{I(x)} - \mu_{a}} \right)^{2}}{2\sigma_{a}^{2}}} \right\rbrack}}} \right\}}}\end{matrix}$$\mspace{20mu} {T_{j,x}^{(t)}:={{P\left( {{{A(x)} = {\left. j \middle| I \right. = {I(x)}}};\theta^{(t)}} \right)} = \frac{\tau_{j}^{(t)}{G\left( {{{I(x)};\mu_{j}^{(t)}},\sigma_{j}^{(t)}} \right)}}{\sum\limits_{a = 1}^{N_{analytes}}{\tau_{a}^{(t)}{G\left( {{{I(x)};\mu_{a}^{(t)}},\sigma_{a}^{(t)}} \right)}}}}}$  (membership  probabilities) $\mspace{20mu} \begin{matrix}{{Q\left( \theta \middle| \theta^{(t)} \right)} = {E\left\lbrack {\ln \; {L\left( {{\theta;I},A} \right)}} \right\rbrack}} \\{= {E\left\lbrack {\ln {\prod\limits_{x = 1}^{N_{pixels}}{L\left( {{\theta;{I(x)}},{A(x)}} \right)}}} \right\rbrack}} \\{= {\sum\limits_{x = 1}^{N_{pixels}}{E\left\lbrack {\ln \; {L\left( {{\theta;{I(x)}},{A(x)}} \right)}} \right\rbrack}}} \\{= {\sum\limits_{a = 1}^{N_{analytes}}{\sum\limits_{x = 1}^{N_{pixels}}{T_{a,x}^{(t)}\left\lbrack {{\ln \; \tau_{a}} - {1\; \frac{\ln \left( {2\pi \; \sigma_{a}^{2}} \right)}{2}} - \frac{\left( {{I(x)} - \mu_{a}} \right)^{2}}{2\sigma_{a}^{2}}} \right\rbrack}}}}\end{matrix}$$\mspace{20mu} {\tau^{({t + 1})} = {\underset{\tau}{argmax}\left\{ {\sum\limits_{a = 1}^{N_{analytes}}\left( {\left\lbrack {\sum\limits_{x = 1}^{N_{pixels}}T_{a,x}^{(t)}} \right\rbrack \ln \; \tau_{a}} \right)} \right\}}}$$\mspace{20mu} {\tau_{j}^{({t + 1})} = {{\frac{1}{N_{pixels}}{\sum\limits_{x = 1}^{N_{pixels}}{T_{j,x}^{(t)}\left( {\mu_{j}^{({t + 1})},\sigma_{j}^{({t + 1})}} \right)}}} = {\underset{\mu,\sigma}{argmax}\left\{ {\sum\limits_{a = 1}^{N_{analytes}}\left( {\left\lbrack {\sum\limits_{x = 1}^{N_{pixels}}T_{a,x}^{(t)}} \right\rbrack\left\lbrack {{{- 1}\; \frac{\ln \left( {2\pi \; \sigma_{a}^{2}} \right)}{2}} - \frac{\left( {{I(x)} - \mu_{a}} \right)^{2}}{2\sigma_{a}^{2}}} \right\rbrack} \right)} \right\}}}}$$\mspace{20mu} {\mu_{j}^{({t + 1})} = \frac{\sum\limits_{x = 1}^{N_{pixels}}{T_{j,{I{(x)}}}^{(t)}{I(x)}}}{\sum\limits_{x = 1}^{N_{pixels}}T_{j,{I{(x)}}}^{(t)}}}$$\mspace{20mu} {\sigma_{j}^{({t + 1})} = \frac{\sum\limits_{x = 1}^{N_{pixels}}{T_{j,{I{(x)}}}^{(t)}\left( {{I(x)} - \mu_{j}^{({t + 1})}} \right)}^{2}}{\sum\limits_{x = 1}^{N_{pixels}}T_{j,{I{(x)}}}^{(t)}}}$

The main problem with this simple model is that it doesn't code anyhigher order structure to the pixels. There is no prior probabilityassociated with more realistic arrangements of pixels. Only taudetermines the proportion of analyte classes. Thus, once can use the tauvariable to insert in the blob prior probability model, in particular atthe step of updating membership probabilities.

Thus a modified Bayesisan inference procedure may be applied with a muchmore sophisticated Bayesian prior. In the basic EM implementation, thereis no real prior distribution. The variable tau represents the a priorirelative proportion of each class but even this variable is unspecifiedand estimated during the inference procedure. Thus, there is no a prioribelief about the distribution of classes in the basic EM model. In ourmodel, the model prior is represented by the multi-scale analyte model.Tau becomes a function of position (and other variables), not just aglobal proportion.

$\begin{matrix}\begin{matrix}{\mspace{20mu} {{L\left( {{\theta;I},A} \right)} = {f\left( {I,\left. A \middle| \theta \right.} \right)}}} \\{= {{f(A)}{f\left( {\left. I \middle| A \right.,\theta} \right)}}} \\{= {{f(A)}{\prod\limits_{x = 1}^{N_{pixels}}{G\left( {{{I(x)};\mu_{A{(x)}}},\sigma_{A{(x)}}} \right)}}}} \\{= {{f(A)}\exp \left\{ {{\sum\limits_{x = 1}^{N_{pixels}}{- \frac{\ln \left( {2\pi \; \sigma_{a}^{2}} \right)}{2}}} - \frac{\left( {{I(x)} - \mu_{a}} \right)^{2}}{2}} \right\}}}\end{matrix} & \; \\\begin{matrix}{\mspace{20mu} {{Q\left( \theta \middle| \theta^{(t)} \right)} = {E\left\lbrack {\ln \; {L\left( {{\theta;I},A} \right)}} \right\rbrack}}} \\{= {E\left\lbrack {{\ln \; {f(A)}} + {\sum\limits_{x = 1}^{N_{pixels}}{- \frac{\ln \left( {2\pi \; \sigma_{a}^{2}} \right)}{2}}} - \frac{\left( {{I(x)} - \mu_{a}} \right)^{2}}{2\sigma_{a}^{2}}} \right\rbrack}} \\{= {{E\left\lbrack {\ln \; {f(A)}} \right\rbrack} +}} \\{{\sum\limits_{a = 1}^{N_{analytes}}{\sum\limits_{x = 1}^{N_{pixels}}{T_{a,x}^{(t)}\left\lbrack {{{- 1}\; \frac{\ln \left( {2\pi \; \sigma_{a}^{2}} \right)}{2}} - \frac{\left( {{I(x)} - \mu_{a}} \right)^{2}}{2\sigma_{a}^{2}}} \right\rbrack}}}}\end{matrix} & \; \\{{\left( {\mu_{j}^{({t + 1})},\sigma_{j}^{({t + 1})}} \right) = {\underset{\mu,\sigma}{argmax}\left\{ {\sum\limits_{a = 1}^{N_{analytes}}\left( {\left\lbrack {\sum\limits_{x = 1}^{N_{pixels}}T_{a,x}^{(t)}} \right\rbrack\left\lbrack {{{- 1}\; \frac{\ln \left( {2\pi \; \sigma_{a}^{2}} \right)}{2}} - \frac{\left( {{I(x)} - \mu_{a}} \right)^{2}}{2\sigma_{a}^{2}}} \right\rbrack} \right)} \right\}}}\mspace{20mu} {\mu_{j}^{({t + 1})} = \frac{\sum\limits_{x = 1}^{N_{pixels}}{T_{j,{I{(x)}}}^{(t)}{I(x)}}}{\sum\limits_{x = 1}^{N_{pixels}}T_{j,{I{(x)}}}^{(t)}}}\mspace{20mu} {\sigma_{j}^{({t + 1})} = \frac{\sum\limits_{x = 1}^{N_{pixels}}{T_{j,{I{(x)}}}^{(t)}\left( {{I(x)} - \mu_{j}^{({t + 1})}} \right)}^{2}}{\sum\limits_{x = 1}^{N_{pixels}}T_{j,{I{(x)}}}^{(t)}}}} & \;\end{matrix}$

The membership probability function is defined as follows:

${f\left( {I,\left. A \middle| \theta \right.} \right)} = {{{f(A)}{f\left( {\left. I \middle| A \right.,\theta} \right)}} = {{f(A)}{\sum\limits_{x = 1}^{N_{pixels}}{G\left( {{{I(x)};\mu_{A{(x)}}},\sigma_{A{(x)}}} \right)}}}}$${f\left( {\left. A \middle| I \right.,\theta} \right)} = {\frac{1}{Z}{f(A)}{f\left( {\left. I \middle| A \right.,\theta} \right)}}$${P\left( {{{A(x)} = {\left. j \middle| {I(x)} \right. = i}},\theta} \right)} = {\frac{1}{Z}{P\left( {{A(x)} = j} \right)}{f\left( {{{I(x)} = {\left. i \middle| {A(x)} \right. = j}},\theta} \right)}}$T_(j, x)^((t)) := P(A(x)^((t)) = j|I(x) = i, θ)$T_{j,x}^{(t)}:=\frac{{P\left( {{A(x)}^{(t)} = j} \right)}{G\left( {{{I(x)};\mu_{j}^{(t)}},\sigma_{j}^{(t)}} \right)}}{\sum\limits_{a = 1}^{N_{analytes}}{{P\left( {{A(x)}^{(t)} = a} \right)}{G\left( {{{I(x)};\mu_{a}^{(t)}},\sigma_{a}^{(t)}} \right)}}}$(membership  probabilities) $\begin{matrix}{T_{j,x}^{(t)}:={\frac{1}{Z}{\underset{models}{E}\left\lbrack {{P\left( {{A(x)} = j} \right)}{P\left( {{{I(x)} = {\left. i \middle| {A(x)} \right. = j}},\theta} \right)}} \right\rbrack}}} \\{:={\frac{1}{Z}{\sum\limits_{\alpha \in {models}}{{P\left( {A = \alpha} \right)}{P\left( {{A(x)} = j} \right)}{P\left( {{{I(x)} = {\left. i \middle| {A(x)} \right. = j}},\theta} \right)}}}}} \\{:={\frac{1}{Z}{\sum\limits_{\alpha \in {models}}{{P(N)}{\prod{{f\left( C_{c} \right)}{\prod{{f\left( B_{b} \right)}{P\left( {{A(x)} = j} \right)}}}}}}}}} \\{{P\left( {{{I(x)} = {\left. i \middle| {A(x)} \right. = j}},\theta} \right)}}\end{matrix}$

The inference algorithm is as follows. At each step of iteration, themembership probability map is initialized to zero so that all classeshave zero probability. Then for all possible model configurations, themembership probability map may be added to as follows:

T _(j,x) ^((t)) +=P(N ^((t)))Πf(C _(c) ^((t)) Πf(B _(b)^((t)))P(A(x)^((t)) =j)P(I(x)=i|A(x)^((t)) =j,θ)

Finally, the probability vector may be normalized at each pixel in themembership probability map to restore the completeness assumption.Advantageously one can iterate over all model configurations. This isdone by sequentially considering values for N from 0 to a relatively lowvalue, for instance 9, at which point extremely few sections have everbeen observed to have as many blobs. For each value of N one can examinedifferent putative blob configurations. The putative blobs may bethresholded to a small number (N) based on their individual blobprobabilities. Then, all of the permutations of N blobs are considered.Thus, one can simultaneously considering all of the most likely blobconfigurations and weighting each model by its prior probability. Thisprocedure is obviously an approximate inference scheme since the fullspace of multi-scale model configurations may not be considered. One canassume, however, that by considering the most likely (in terms of both Nand blobs), a good approximation is achieved. This procedure alsoassumes that the weighted average of the most likely configurationsprovides a good estimate at each individual pixel. Another alternativeis to perform a constrained search of model configurations and selectthe highest likelihood model as the MAP (maximium a posteriori)estimate.

Further exemplary statistical models (e.g., the posterior P(A\I)) arealso described herein. In a CT angiography the following information maybe available:

-   -   Intensity        -   CT Hounsfield units or MR intensities        -   Possibly other imaging features    -   Position relative to anatomy        -   Where in the plaque a pixel is    -   Neighboring pixels        -   E.g., for smoothing contours through level sets

Posterior probability may be computed as:

P(A/I)∝P(I|A)·P(A)

Thus, the following image information may influences analyteprobability, Ai(x)

-   -   I(x) is observed image intensity (possibly a vector)    -   T(x) is observed relative wall thickness from image segmentation    -   F(x) are CT image features    -   S(x) are features of vessel wall shape (e.g., luminal bulge)

In some embodiments a Metropolis-Hastings like approach may be utilized.In other embodiments a maximum a posteriori approach may be applied.

The following are example algorithmic possibility for a statisticalanalysis model. In some embodiments, the model may utilize Beliefpropagation (AKA max sum, max product, sum product messaging). Thus, forexample a Viterbi (HMM) type approach may be utilized, e.g., wherein,hidden states are the analyte assignments, A, Observed states are theimage intensities, I. This approach may advantageously find a MAPestimate may be argmax P (A|I). In some embodiments a soft outputViterbi algorithm (SOVA) may be utilized. Note that reliability of eachdecision may be indicated by difference between chosen (survivor) pathand discarded path. Thus, this could indicate reliability of each pixelanalyte classification. In further example embodiments aforward/backward Baum-Welch (HMM) approach may be utilized. For exampleone can compute most likely state at any point in time but not the mostlikely sequence (see Viterbi).

Another possible technique is the Metropolis-Hastings (MCMC) approach,e.g., wherein one repeatedly samples A and weights by likelihood andprior. In some embodiments, a simple MRF version for sampling may beutilized. Note that it may be particularly advantageous to sample theposterior directly. In example embodiments, one can build up per-pixelhistograms of analyte class.

Other algorithm possibilities include applying a Gibbs Sampler,Variational Bayes (similar to EM), Mean field approximation, a Kalmanfilter, or other techniques.

As noted above, in some embodiments an Expectation Maximization (EM)posterior approach may be utilized. Under this approach, observed dataXis the imaging values, unknown parameters θ are due to the analyte map(but not including analyte probabilities) and latent variable Z is theanalyte probability vector. One key feature of this approach is that itenables iterating between estimating class membership (Z) and modelparameters (θ) since they each depend on each other. However, since theanalyte map separates out analyte probabilities, the approach may bemodified such that the current class membership doesn't have toinfluence the model parameters (since these are learned this during atraining step). Thus, EM basically learning the model parameters as ititerates through the current data. Advantageously, exemplaryimplementation of the EM approach iteratively compute maximum likelihoodbut assumes a flat prior.

Techniques are also provided herein for representing longitudinalcovariance. Due to wide spacing of histology slices (e.g., 4 mm),sampling may not faithfully capture the longitudinal variation inanalytes. However, 3D image analysis is typically performed andpresumably there is some true longitudinal covariance. The problem isthat histological information typically isn't provided for longitudinalcovariance. Nonetheless the exemplary statistical models disclosedherein may reflect a slow variation in longitudinal direction.

In some embodiments, a Markov model/chain may be applied. FIG. 15depicts exemplary implantation of a 1D Markov chain for Text/DNA.Conventionally, when applied to images in MRF Markov chains are typicalas low order as possible. A higher order chain may be advantageous,however, due to conditional independence (Markov property). Otherwisethe data may be too scrambled to be of value. This is demonstrated bythe 1D sampling of an exemplary Markov chain as applied to text:

-   -   Uniform probability sampling output:        -   earryjnv anr            jakroyvnbqkrxtgashqtzifzstqaqwgktlfgidmxxaxmmhzmgbya            mjgxnlyattvc rwpsszwfhimovkvgknlgddou nmytnxpvdescbg k            syfdhwqdrj jmcovoyodzkco inlycehpcqpuflje            xkcykcwbdaifculiluyqerxfwlmpvtlyqkv    -   0-order Markov chain output:        -   ooyusdii eltgotoroo tih ohnnattti gyagditghreay nm            roefnnasos r naa euuecocrrfca ayas el s yba anoropnn laeo            piileo hssiod idlif beeghec ebnnioouhuehinely neiis            cnitcwasohs ooglpyocp h trog 1    -   1^(st) order Markov chain output:        -   icke inginatenc blof ade and jalorghe y at helmin by hem            owery fa st sin r d n cke s t w anks hinioro e orin en s ar            whes ore jot j whede chrve blan ted sesourethegebe inaberens            s ichath fle watt o    -   2^(nd) order Markov chain output:        -   he ton th a s my caroodif flows an the er ity thayertione            wil ha m othenre re creara quichow mushing whe so mosing            bloack abeenem used she sighembs inglis day p wer wharon the            graiddid wor thad k    -   3^(rd) order Markov chain output:        -   es in angull o shoppinjust stees ther a kercourats allech is            hote ternal liked be weavy because in coy mrs hand room him            rolio und ceran in that he mound a dishine when what to            bitcho way forgot p

FIG. 16 depicts an example first order Markov chain for a textprobability table. Note that such tables are exponentially sized interms of order:

-   -   D=order of Markov chain    -   N=number of letters    -   Size=N^(D)

Thus, higher order leads to problems with dimensionality. Advantageouslyhistology samples have a very high resolution. However, since histologysamples are not statistically independent, this may lead to overfittingas later described in greater detail. In general, the more conditionaldependence that is modeled, the more predictive the model can be.

In example embodiments, a 2D Markov random field (MRF) may be used forpixel values instead of a 1D sequence such as for letters. FIG. 17depicts conditional dependence of a first pixel (black) based on itsneighboring pixels (gray). In example embodiments cliques may make usesymmetry to reduce the number of dependencies in half. In someembodiments, the values of pixels could be simple image intensities orcould be probability values for classification problems. Problems existwith typical MRF use. Conventional MRF almost always is limited to thenearest neighbor pixels providing conditional dependence which greatlyreduces the specificity of the probability space represented; usuallyjust black/white blobs for very general purpose segmentation/filtering;extremely short range dependencies. However, whereas pixels are highlydiscretized a blob just missing one pixel and falling in the next maycompletely change the probability distribution. Thus a real imagestructure is much more continuous than is typically accounted for usingMRF.

For this reasons the systems and methods of the present disclosure mayadvantageously utilize an inference procedure, e.g., a Bayes type ruleof Posterior a Likelihood×Prior (P(A/I) α P(I/A)×P(A)). Using acrossword type analogy, the inference procedure implemented by thesystems and methods of the subject application is a bit like trying toOCR a crossword puzzle from a noisy scan. Knowledge (even imperfectknowledge of several squares may help inform an unknown square in thecrossword puzzle. Efficiently is improved even more by considering bothvertical and horizontal direction simultaneously. In exampleembodiments, the inference procedure may be heuristic. For example, onecan initialize with uninformed prior, then, solve the easier ones first,which gives you clues about the harder ones which are solved later. Thuseasy to detect biological properties such as calcium may inform theexistence of other harder to detect analytes such as lipids. Each stepof the inference procedure may narrow the probability distributions forunsolved pixels.

As noted above a high order Markov chain is preferable to obtain usabledata. The disadvantage of utilizing a higher order Markov approach isthat there may not be enough data to inform the inference process. Inexample embodiments, this issue may be addressed by utilizing densityestimation methods such as Parzen windowing or utilizing krigingtechniques.

To form an inference procedure one may initialize with unconditionalprior probabilities of analytes and then use a highest level of evidenceto start narrowing down probabilities. For example in some embodiments,an uncertain width may be associate with each analyte probabilityestimate. In other embodiments, closeness to 1/N may represent suchuncertainty.

Notably, the term “Markov” is used loosely herein since the proposedMarkov implementations are not memoryless but rather are explicitlytrying to model long range (spatial) dependencies.

Because the CT resolution is low compared to histology and plaqueanatomy, in some embodiments it may be preferable to utilize acontinuous space (time) Markov model rather than discrete space (time).This may work well with the level set representation of probability mapssince they naturally work well with sub-pixel interpolation. Discreteanalyte states makes the model a discrete space model. However, if onerepresents continuous probabilities rather than analytepresence/absence, then it becomes a continuous space model.

Turning to lung based applications, table 4 below depicts exemplarybiological properties/analytes which may utilized with respect to ahierarchical analytics framework for such applications.

TABLE 5 Biologically-objective measurands Supported by lung basedapplications Category Description Readings Units/Categories Size Thesize of the Volume (lesion, solid mm{circumflex over ( )}3 lesionportion, ground-glass portion) Longest diameter and mm perpendicular(lesion, solid portion, ground- glass portion) Shape/ Overall shape ofthe Shape sphericity (unitless: Margin lesion and round = 1, oval ~0.5,descriptions of its line = 0)/lobulated- border which mayirregular/cavitary, indicate certain speculation, notch/cut cancers ordiseases Margin Tumor margin scale (HU) (possibly including Tumor marginwindow fibrotic scarring) (HU/mm) Topology Euler Number Solidity Meandevelopment Volume % solid of % of cell types or lack Lesion (C/T ratio)thereof that make up Volume % ground-glass % the lesion of Lesion(differentiation, Solid density g/ml organization) Ground glass densityg/ml Mass of solid g Mass of ground glass g Heterogeneity Covariance andSD (variation of solid g/ml development of cell density) types or lackthereof SD (variation of ground g/ml that make up the glass) lesionPattern Nonsolid or ground-glass (differentiation, opacity (pure GGN)/organization) perifissural/part-solid (mixed GGN)/solid Solid portionpattern Radial intensity distribution 1^(st) and 2^(nd) order statistics(Central/ central with ring/diffuse/ peripheral) Spatial coherence NSM(non-spatial (texture, “clumpiness”, methods); SGLM (apatial localizedheterogeneity) gray-level methods) e.g., Haralick; fractal analysis(FA): Lacunarity, average local variance, variance of local variance,average of local average; filters & transfroms (F&T) e.g., GaborInvasive Measure of Lesion's Pleural contact length mm Potentialinvasive extent or (AKA arch distance) potential extent Pleural contactlength- unitless to-maximum lesion diameter Pleural InvolvementDisplacement from expected location Lobe Location Upper/middle/lowerlobe//right/left Lobe centrality unitless (1 = lobe center, 0 = lobeboundary) Airway Involvement/air category bronchogram Vascular changesDilated/rigid/convergent/ tortuous Calcification Response to Volumemm{circumflex over ( )}3 injurious agent Volume % of Lesion %(dystrophic) or Distribution Central/peripheral/ caused by derangeddiffuse metabolism Pattern amorphous/punctuate/ (metastatic)reticular/popcorn/ laminated Cell Measures of cell Uptake SUV(unitless), % ID/g Metabolism metabolism Glycolytc volume <each non-Change assessed Pairwise arithmetic In units of measurand categoricalbetween as few as 2 difference measurand but arbitrarily many Pairwiseratio unitless above> timepoints Pairwise doubling timedays/weeks/months Polynomial fit coefficients Non-arithmetic changeassessment with registration, e.g., vascular changes <each non- Assessedover Total Tumor Burden mm{circumflex over ( )}3 categorical multipletargets Tumor Number unitless measurand according to MultilobarTrue/false above> response criteria, Lymph Node status category e.g.,RECIST, Metastasis category WHO, etc. Response category

In particular, systems may be configured to detect lung lesions. Thus,an exemplary system may be configured for whole lung segmentation. Insome embodiments, this may involve use of minimum curvature evolution tosolve juxtapleural lesion problems. In some embodiments, the system mayimplement lung component analysis (vessel, fissure, bronchi, lesionetc.). Advantageously a Hessian filter may be utilized to facilitatelung component analysis. In some embodiments lung component analysis mayfurther include pleural involvement, e.g., as a function of fissuregeometry. In further embodiments, attachment to anatomic structures mayalso be considered. In addition to lung component analysis, separateanalysis of ground glass vs. solid stated may also be applied. This mayinclude determination of geometric features, such as volume, diameter,sphericity, image features, such as density and mass, and fractalanalysis.

Fractal analysis may be used to infer lepidic growth patterns. In orderto perform fractal analysis on very small regions of interest, ourmethod adaptively modifies the support for convolution kernels to limitthem to the region of interest (i.e., lung nodule). Intersectingvessels/bronchi as well as non-lesion feature may be masked out for thepurposes of fractal analysis. This is done by applying IIR Gaussianfilters over masked local neighborhoods and normalizing with IIR blurredbinary masking. In some embodiments, fractal analysis may furtherinclude determining lacunarity (based on variance of the local mean).This may be applied with respect to lung lesions, subparts of lesions.In example embodiments, IIR Gaussian filters or circular neighborhoodsmay be applied. In some embodiments IIR may be utilized to computevariance. Average of local variance (AVL) may also be computed, e.g., asapplied to lung lesions. Likewise, a variance of local variance may becalculated.

In example embodiments, both lesion structure and composition may becalculated. Advantageously calculating lesion structure may utilize fullvolumetry of this sections thereby improving on calculating sizemeasurement change. Measurements such as sub-solid and ground glassopacity (GGO) volume may also be determined as part of assessing lesionstructure. Turning to lesion composition, tissue characteristics such asconsolidation, invasion, proximity and perfusion may be calculated e.g.,thereby reducing false positive rate relative to conventional analytics.

With reference now to FIG. 18, a further exemplary hierarchicalanalytics framework 1800 for the systems of the present disclosure isdepicted. FIG. 18 may be understood as an elaboration of FIG. 1elucidating greater detail with respect to exemplary intermediateprocessing layers of the hierarchical inference system. Advantageouslythe hierarchical inferences still flow from imaging data 1810 tounderlying biological information 1820 to clinical disease 1800.Notably, however, the framework 1800 includes multiple levels of datapoints for processing imaging data in order to determine biologicalproperties/analytes. At a pre-processing level 1812, physicalparameters, registrations transformations and region segmentations maybe determined. This preprocessed imaging information may then beutilized to extract imaging features at the next level of data points1814 such as intensity features, shape, texture, temporalcharacteristics, and the like. Extracted image features may nextutilized at level 1816 to fit one or more biological models to theimaged anatomy. Example models may include a Bayes/Markov net lesionsubstructure, a fractal growth model, or other models such as describedherein. The biological model may advantageously act as a bridge forcorrelating imaging features to underlying biologicalproperties/analytes at level 1822. Example biologicalproperties/analytes include anatomic structure, tissue composition,biological function, gene expression correlates, and the like. Finally,at level 1832 the biological properties/analytes may be utilized todetermine clinical findings related to the pathology including, e.g.,related to disease subtype, prognosis, decision support and the like.

1. A system comprising a processor a non-transient storage mediumincluding processor executable instructions implementing an analyzermodule including a hierarchical analytics framework configured to:utilize a first set of algorithms identify and quantify a set ofbiological properties utilizing imaging data and; utilize a second setof algorithms to identify and characterize one or more medicalconditions based on the quantified biological properties.
 2. The systemof claim 1, wherein the analytics framework implements an algorithm foridentifying and characterizing the one or more medical conditions basedon the quantified biological properties wherein a training set from oneor more non-radiological or non-imaging data sources was used intraining the algorithm.
 3. The system of claim 1, wherein the analyticsframework implements an algorithm for identifying and quantifying thebiological properties utilizing radiological imaging data, wherein atraining set from one or more non-radiological data sources was usedtraining the algorithm.
 4. The system of claim 1, wherein data from aplurality of same or different types of data sources is incorporatedinto the process of identifying and characterizing the one or moremedical conditions.
 5. The system of claim 4, wherein data from one ormore non-imaging data sources is used in conjunction with the imagingdata such that the set of biological properties includes one or morebiological properties identified or quantified based at least in part onthe data from one or more non-imaging data sources.
 6. The system ofclaim 5, wherein the data from non-imaging sources includes one or moreof (i) demographics, (ii) results from cultures or other lab tests,(iii) genomic, proteomic or metabolomic expression profiles, or (iv)diagnostic observations.
 7. The system of claim 4, wherein data from oneor more non-radiological data sources is used in conjunction withradiological imaging data such that the set of biological propertiesincludes one or more biological properties identified or quantifiedbased at least in part on the data from one or more non-radiologicaldata sources.
 8. The system of claim 1, wherein information relating tothe set of identified and quantified biological properties is adjustedafter an initial identification or quantification thereof based oncontextual information which adjusts or updates one or moreprobabilities impacting the identification or quantification of at leastone of the biological properties in the set.
 9. The system of claim 8,wherein the contextual information includes at least one of patientdemographics, correlations relating different biological properties, orcorrelations relating one or more of the identified medical conditionsto one or more biological properties.
 10. The system of claim 1, whereininformation relating to the identified and characterized one or moremedical conditions is adjusted after an initial identification orcharacterization thereof based on contextual information which adjustsor updates one or more probabilities impacting the identification orcharacterization of at least one of one or more medical conditions. 11.The system of claim 1, wherein the system is configured to provide auser with information relating both the one or more medical conditionsas well as relating to the underlying biological properties used in theidentification or characterization of the one or more medicalconditions.
 12. The system of claim 1, wherein the system is configuredto determine at least one of (i) which of the biological parameters inthe set have the greatest amount of uncertainty regarding theidentification or quantification thereof or (ii) which of the biologicalparameters in the set are most deterministic of the identification orcharacterization of the one or more medical conditions.
 13. The systemof claim 1, wherein the identifying and quantifying the set ofbiological properties utilizing the imaging data includes receivingpatient data including the image data and parsing the received data intoa set of empirical parameters including one or more imaging features ofan imaged target.
 14. The system of claim 13, wherein the parsing thereceived data includes pre-processing image data including performingone or more of: (i) intensity vector analysis, (ii) image registrationand transformation analysis or (iii) anatomic region analysis.
 15. Thesystem of claim 13, wherein the imaging features are derived based onone or more of: (i) temporal operators, (ii) fractal analysis, (iii)spatial operators or (iv) or an augmented Markov analysis.
 16. Thesystem of claim 1, wherein an imaged target is a lesion and wherein thebiological properties include (i) a size of the lesion, (ii) a shape ofthe lesion, (iii) a characterization of the margin of the lesion, (iv) asolidity of the lesion, (v) a heterogeneity of the lesion, (vi) ameasure of the lesion's invasive extent or potential extent, (vii) acompositional measure of calcification related to the lesion and (viii)a measure of cell metabolism with respect to the lesion.
 17. The systemof claim 16, wherein at least one or the biological properties isquantified by (i) assessing change between a plurality of timepoints or(ii) assessing differences between a plurality of targets.
 18. Thesystem of claim 1, wherein an imaged target is a blood vessel andwherein the biological properties include (i) an indication of plaquecoverage of the vessel wall, (ii) an indication of stenosis of thevessel wall, (iii) an indication of dilation of the vessel wall, and(iv) an indication of vessel wall thickness.
 19. The system of claim 1,wherein an imaged target is a vascular tissue and wherein the biologicalproperties include (i) an indication of a lipid core of the vascular orrelated tissue, (ii) a measure of fibrosis of the vascular or relatedtissue, (iii) a measure of calcification of the vascular or relatedtissue, (iv) an indication of any hemorrhage in the vascular or relatedtissue, (v) a measure of permeability of the vascular or related tissue,(vi) an indication of thrombosis of the vascular or related tissue, and(vii) an indication of ulceration of the vascular or related tissue. 20.The system of claim 1, wherein set of biological properties includes oneor more anatomical, morphological, structural, compositional,functional, chemical, biochemical, physiological, histological orgenetic characteristics.
 21. The system of claim 1, wherein thecharacterization of the one or more medical conditions includesphenotyping the medical conditions.
 22. The system of claim 21, whereinthe characterization of the one or more medical conditions furtherincludes determining predictive outcomes for the medical conditions. 23.The system of claim 22, wherein the one or more predictive outcomes arepredicated on a predetermined causality rating between phenotypes andthe predictive outcomes.
 24. The system of claim 1, wherein the storagemedium further includes processor executable instructions implementing atrainer module, for training one or more algorithms implemented by thehierarchical analytics framework.
 25. The system of claim 1, wherein thestorage medium further includes processor executable instructionsimplementing a cohort module for enabling a user to define one or morecohort groupings of individuals for further analysis.
 26. The system ofclaim 1, wherein the analyzer module includes algorithms for calculatingimaging features from the imaging data, wherein some of the imagingfeatures are computed on a per-pixel basis, while other imaging featuresare computed on a region-of-interest basis.
 27. The system of claim 1,wherein the first set of algorithms is distinctly trained from thesecond set of algorithms.
 28. The system of claim 1, wherein at leastone of the algorithms in the first and second sets of algorithms isderived utilizing machine learning.
 29. The system of claim 1, whereinat least one of the algorithms in the first and second sets ofalgorithms is characterized by one or more of neural nets, SVMs, partialleast squares, principle components analysis or random forests.
 30. Thesystem of claim 1, wherein the analyzer module is configured to enabledelineating of a field for the imaging data.
 31. The system of claim 30,wherein the delineating the field includes segmenting one of organs,vessels, lesion or other application-specific anatomical features. 32.The system of claim 30, wherein the field is a cross-sectional slice ofa blood vessel.
 33. The system of claim 31, wherein the analyzer moduleis further configured to delineate a target in the field and determininganatomic structure or composition characteristics for the target,wherein the target is a blob in the cross-sectional slice of a bloodvessel.
 34. The system of claim 1, wherein the hierarchical analyticsframework includes fitting a biological model utilizing the imaging datawherein the biological model is then utilized to identify and quantifythe biological properties.
 35. The system of claim 34, wherein the modelis a fractal model.
 36. The system of claim 34, wherein the model isbased on hybrid Bayesian/Markovian network.
 37. The system of claim 34,wherein the model computes biological parameters one or more contiguousregions of a given analyte type.
 38. The system of claim 37, wherein themodel further computes biological parameters based on relationshipsbetween two- or more different contiguous regions of a given analytetype or given analyte types.
 39. The system of claim 38, wherein themodel further computed biological parameters based on a number ofcontiguous regions of a given analyte type or given analyte types. 40.The system of claim 34, wherein the model employs expectationmaximization which accounts for conditional dependence between pixels.41. A non-transient storage medium including processor executableinstructions for: receiving patient data including a set of empiricalparameters, the set of empirical parameters including one or moreimaging features of an imaged target; utilizing a first algorithm toidentify and quantify one or more logical characteristics indicated bythe empirical parameters, the logical characteristics representingpathological features; identifying a set of pathological features, theset of pathological features including the one or more quantifiedlogical characteristics; and utilizing a second algorithm to identifyone or more pathologies indicated by the set of pathological features.42. The non-transient storage medium of claim 41 wherein the identifyingthe one or more pathologies includes identifying a phenotype for eachpathology.
 43. The non-transient storage medium of claim 41 wherein theone or more logical characteristics include one or more morphological,developmental, biochemical or physiological characteristics of theimaged target.
 44. The non-transient storage medium of claim 41 whereinthe first algorithm includes a scoring algorithm for determining aconfidence weighting for each of the logical characteristics.
 45. Thenon-transient storage medium of claim 44 wherein the confidenceweighting for each logical characteristic includes a confidenceweighting for a quantification of that logical characteristic.
 46. Thenon-transient storage medium of claim 45 wherein the confidenceweighting for the quantification of the logical characteristic isdetermined according to a probability distribution across a range ofvalues for the logical characteristic.
 47. The non-transient storagemedium of claim 44 wherein a confidence threshold is utilized toidentify the logical characteristics indicated by the empiricalparameters.
 48. The non-transient storage medium of claim 41 wherein thefirst algorithm is derived utilizing a training collection of aplurality of sets of empirical parameters each with associated withknown quantifications of one or more pathological features.
 49. Thenon-transient storage medium of claim 41 wherein the second algorithmincludes a scoring algorithm for determining a confidence weighting foreach of the pathologies.
 50. The non-transient storage medium of claim49 wherein the confidence weighting for each pathology includes aconfidence weighting for a phenotype thereof.
 51. The non-transientstorage medium of claim 50 wherein the confidence weighting for thephenotype is determined according to a probability distribution across arange of phenotypes for the pathology.
 52. The non-transient storagemedium of claim 49 wherein a confidence threshold is utilized toidentify the pathologies indicated by the pathological features.
 53. Thenon-transient storage medium of claim 49 wherein an initial confidenceweighting in a first pathology is used to adjust an initial confidenceweighting in a second related pathology.
 54. The non-transient storagemedium of claim 53 wherein an initial confidence weighting in the firstpathology is used to adjust an initial confidence weighting in a logicalcharacteristic and wherein the adjusted confidence weighting in thelogical characteristic is used to indicate the second related pathology.55. The non-transient storage medium of claim 41 wherein the set ofempirical parameters further includes one or more of: (i) demographics,(ii) results from cultures or other lab tests, (iii) genomic, proteomicor metabolomic expression profiles, or (iv) diagnostic observations. 56.The non-transient storage medium of claim 41 wherein the logicalcharacteristics include values for quantitative biological analytes. 57.The non-transient storage medium of claim 41 wherein the first andsecond algorithms are derived utilizing machine learning.
 58. Thenon-transient storage medium of claim 57 wherein the first and secondalgorithms are characterized by one or more of neural nets, SVMs,partial least squares, principle components analysis, random forests.59. The non-transient storage medium of claim 41 wherein the first andsecond algorithms are trained utilizing one or more of empirical data orexpert opinion.
 60. The non-transient storage medium of claim 41 whereinthe first and second algorithms are characterized by one or more ofmachine learning, decision trees, differential equations, polynomialexpressions, pattern matching or parsing, dynamic programming, or statespace searches.
 61. The non-transient storage medium of claim 41 furtherincluding processor executable instructions for determining one or morepredictive outcomes for the identified pathologies.
 62. Thenon-transient storage medium of claim 61 wherein the one or morepredictive outcomes for the identified pathologies are determined basedon identifying a phenotype for each pathology.
 63. The non-transientstorage medium of claim 62 wherein the one or more predictive outcomesare predicated on a predetermined causality rating between theidentified phenotypes and the predictive outcomes.
 64. The non-transientstorage medium of claim 41 further including processor executableinstructions for pre-processing image data including performing one ormore of: (i) intensity vector analysis, (ii) image registration andtransformation analysis or (iii) anatomic region analysis.
 65. Thenon-transient storage medium of claim 41 wherein the imaging featuresare derived based on one or more of: (i) temporal operators, (ii)fractal analysis, (iii) spatial operators or (iv) or an augmented Markovanalysis.
 66. The non-transient storage medium of claim 41 wherein thelogical parameters include one or more of (i) size and/or structure,(ii) composition, (iii) hemodynamics, or (iii) gene expressioncorrelates.
 67. The non-transient storage medium of claim 41 wherein theimaged target is a lesion and wherein the one or more logicalcharacteristics include (i) a size of the lesion, (ii) a shape of thelesion, (iii) a characterization of the margin of the lesion, (iv) asolidity of the lesion, (v) a heterogeneity of the lesion, (vi) ameasure of the lesion's invasive extent or potential extent, (vii) ameasure of calcification related to the lesion and (viii) a measure ofcell metabolism with respect to the lesion.
 68. The non-transientstorage medium of claim 67 wherein quantification of the one or morelogical characteristics includes one or more of (i) assessing changebetween a plurality of timepoints or (ii) assessing differences betweena plurality of targets.
 69. The non-transient storage medium of claim 1wherein the imaged target is a blood vessel and wherein the one or morelogical characteristics include (i) an indication of plaque coverage ofthe vessel wall, (ii) an indication of stenosis of the vessel wall,(iii) an indication of dilation of the vessel wall, and (iv) anindication of vessel wall thickness.
 70. The non-transient storagemedium of claim 41 wherein the imaged target is a vascular tissue andwherein the one or more logical characteristics include (i) anindication of a lipid core of the vascular or related tissue, (ii) ameasure of fibrosis of the vascular or related tissue, (iii) a measureof calcification of the vascular or related tissue, (iv) an indicationof any hemorrhage in the vascular or related tissue, (v) a measure ofpermeability of the vascular or related tissue, (vi) an indication ofthrombosis of the vascular or related tissue, and (vii) an indication ofulceration of the vascular or related tissue.
 71. A system comprising:an imaging device for imaging a target; a processor configured for: (i)receiving patient data including a set of empirical parameters, the setof empirical parameters including one or more imaging features of theimaged target; (ii) utilizing a first machine learned algorithm toidentify and quantify one or more logical characteristics indicated bythe empirical parameters, the logical characteristics representingpathological features; (iii) identifying a set of pathological features,the set of pathological features including the one or more quantifiedlogical characteristics; and (iv) utilizing a second machine learnedalgorithm to identify one or more pathologies indicated by the set ofpathological features; and a user interface for outputting informationrelating to the one or more identified pathologies.
 72. A processorenabled method comprising: identifying a set of empirical parameters,the set of empirical parameters including one or more imaging featuresof the imaged target; utilizing a first machine learned algorithm toidentify and quantify one or more logical characteristics indicated bythe empirical parameters, the logical characteristics representingpathological features; identifying a set of pathological features, theset of pathological features including the one or more quantifiedlogical characteristics; and utilizing a second machine learnedalgorithm to identify one or more pathologies indicated by the set ofpathological features.